Description: THE COMPANY For over 40 years, Aero Simulation, Inc. (ASI) has provided quality flight training devices to the US Military. ASI has successfully built numerous training systems with a primary focus on aircrew and maintenance training systems. Our current programs include, but are not limited to, the B-1 Training Systems (Air Force), E-2D Training Systems (Navy), CH-53E Training Devices (USMC), IMOMS (Coast Guard), Navigation, Seamanship, Shiphandling Trainer - NSST (Navy). To learn more about ASI visit Programs- Aero Simulation, Inc. ASI is a 100% employee-owned engineering and manufacturing company committed to supporting the nation's warfighters. Being 100% employee-owned directly impacts our culture: it drives our decision-making, motivates our teams, increases our productivity, improves retention and contributes to the future success of our company. Our culture is one where we work hard for our clients and for each other - and we have fun collaborating, sharing experience and expertise, and learning along the way. We strive to deliver exceptional quality, elevate client relationships, and enrich the careers and lives of our employee-owners. As Employee-Owners, we are invested in the success and continued development of each other and the company. In addition to the Employee Stock Ownership Plan (ESOP), we offer a flexible work environment, generous paid time off, professional development opportunities, industry competitive compensation, and superior benefits to include medical, dental, 401k and more! ASI is a great place to build a career and grow with a company that is dedicated to quality, service and to fostering a community of support for each and every member of the team. To learn more about this opportunity, keep reading! POSITION SUMMARY The Embedded Systems Engineer will be assigned one or more systems within which replicate the avionics/aircraft systems of the land/sea/air vehicle to be simulated. These systems include communications (radios and ICS), navigation (GPS, TACAN, VOR, etc.), defensive systems (chaff, flares, ECM, etc.), offensive systems (bombs, torpedoes, missiles, etc.), aircraft systems (electrical, hydraulics, etc.), and other subsystems which may be applicable (GPWS, TCAS, IFF, etc.). ESSENTIAL DUTIES AND RESPONSIBILITIES Working in the aerospace engineering subspecialty field of avionics, Avionic engineers apply engineering principles to research, analyze, plan, design, develop, code, implement and test aircraft electrical, computer, data networks, and associated systems. Reviews and prepares engineering and technical analysis, reports, design reviews, change proposals, and other technical documentation. Applies their experience to perform functions such as simulation code development, systems integration, design, configuration management, quality assurance testing, and resource management. Acts as an engineering team member on assigned projects developing engineering procedures and controls, managing project effort, and participation in problem resolution. Interfaces with other engineers, system and program contractors, vendors, and Government representatives regarding the technical aspects of engineering programs/projects. Communicate all ongoing activities and results to other engineers, customers, stakeholders, and management in a positive manner. Read, write, and maintain new and existing software. Follows established company and industry processes and procedures. SUPERVISORY RESPONSIBILITIES This job has no supervisory responsibilities. Requirements: EXPERIENCE REQUIREMENTS Entry - 0 to 3 Years MID- 4 to 7 Years SENIOR - 7 to 10 Years The position requires developing code on Linux and Windows systems. Proficiency with C, C++, or C#. Competency with FORTRAN or Ada a plus. Competency in a scripting environment such as Python, Bash, or PowerShell. Competency in a version control system such as Git or SVN Strong technical understanding with at least one area of specialization expected ADDITIONAL REQUIRED SKILLS AND ABILITIES Ability to develop and maintain positive working relationships with internal and external customers. Ability to adapt communication style and messaging to different audiences. Ability to manage multiple priorities and projects simultaneously, ensuring stakeholder expectations are managed appropriately. Ability to work in a project-oriented, fast paced environment to meet deadlines. The candidate must be able to work in a team environment, or independently, as necessary, and be a self-starter who will attack designs and resolve problems effectively and efficiently. Proficiency in common business software (Microsoft Office - Word, Outlook, Power Point, Excel, Visio, Teams/SharePoint). Ability to work with project teams to successfully deliver new or modified functionalities. Keen attention to detail to deliver solutions that meet business requirements and are operationally supportable. EDUCATION REQUIREMENTS Bachelors or Master's in Electrical or Computer Engineering -or- Equivalent experience to education and three years' experience in related aircraft or flight simulation systems EMPLOYMENT REQUIREMENTS U.S. Citizenship Required: As a federal contractor supporting the U.S. warfighter, it is required that all employees in this role be United States citizens in compliance with government contract and security requirements. Background Check: Must be able to successfully pass an initial background screening. Security Clearance: Must be able to obtain and maintain an active Department of Defense (DoD) security clearance. The selected candidate will be subject to a government security investigation and must meet eligibility requirements for access to classified information, as required by the position. Work Schedule Flexibility: Occasional overtime and alternate shifts may be required to meet customer schedules, critical deadlines, or program milestones. Travel Requirements: Must be able and willing to travel domestically and, on occasion, internationally to customer sites, partner locations, or project meetings in support of contract objectives. Drug-Free Workplace: ASI maintains a Drug-Free Workplace. Applicants and employees are subject to pre-employment and random drug testing in accordance with company policy. Employment Contingency: All employment offers are contingent upon the successful completion of all background checks, security clearance requirements, and drug-screening results. PHYSICAL REQUIREMENTS Candidate must be able to work in small-confined spaces (simulator cockpits) Typically, employees sit comfortably to do their work, interspersed by brief periods of standing, walking, bending carrying papers and books, and extensive periods requiring the use of computer terminals to accomplish work objectives. Additional skills may be required to perform additional task(s) specific to work location, department or line of business. WORK ENVIRONMENT Work is primarily performed in an office environment consisting of offices and cubicles with low to moderate noise and bright or dim lighting. The work is fast-paced and sometimes involves extreme deadline pressures. The nature of the work requires a high degree of teamwork and cooperation with other members of the staff as well as external customers. Note: This job description is not intended to be an exhaustive list of all duties, responsibilities, or qualifications that are required of the employee for the job. Duties, responsibilities, and activities may change at any time with or without notice. The following policies apply to all areas of employment, including recruitment, hiring, training and development, promotion, transfer, termination, layoff, compensation benefits, social and recreational programs, and all other conditions and privileges of employment in accordance with applicable federal, state, and local laws. Candidate Reasonable Accommodation Candidates requiring a reasonable accommodation, as defined by the Americans with Disabilities Act, must notify Aero simulation, Inc. by e-mail at or by calling directly at . ADA Policy It is the policy of ASI to comply with all the relevant and applicable provisions of the Americans with Disabilities Act (ADA) and its Amendments. ASI will not discriminate against any qualified employee or job applicant with respect to any terms, privileges, or conditions of employment because of a person's physical or mental disability. ASI also will make reasonable accommodation wherever necessary for all employees or applicants with handicaps, disabilities, provided that the individual is otherwise qualified to safely perform the duties and assignments connected with the job and provided that any accommodations made are not an undue hardship for ASI. Equal Employment Opportunity ASI is proud to be an Equal Employment Opportunity and Affirmative Action employer dedicated to attracting, retaining, and developing a diverse and inclusive workforce. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, domestic/civil partnership or marital status . click apply for full job details
11/12/2026
Full time
Description: THE COMPANY For over 40 years, Aero Simulation, Inc. (ASI) has provided quality flight training devices to the US Military. ASI has successfully built numerous training systems with a primary focus on aircrew and maintenance training systems. Our current programs include, but are not limited to, the B-1 Training Systems (Air Force), E-2D Training Systems (Navy), CH-53E Training Devices (USMC), IMOMS (Coast Guard), Navigation, Seamanship, Shiphandling Trainer - NSST (Navy). To learn more about ASI visit Programs- Aero Simulation, Inc. ASI is a 100% employee-owned engineering and manufacturing company committed to supporting the nation's warfighters. Being 100% employee-owned directly impacts our culture: it drives our decision-making, motivates our teams, increases our productivity, improves retention and contributes to the future success of our company. Our culture is one where we work hard for our clients and for each other - and we have fun collaborating, sharing experience and expertise, and learning along the way. We strive to deliver exceptional quality, elevate client relationships, and enrich the careers and lives of our employee-owners. As Employee-Owners, we are invested in the success and continued development of each other and the company. In addition to the Employee Stock Ownership Plan (ESOP), we offer a flexible work environment, generous paid time off, professional development opportunities, industry competitive compensation, and superior benefits to include medical, dental, 401k and more! ASI is a great place to build a career and grow with a company that is dedicated to quality, service and to fostering a community of support for each and every member of the team. To learn more about this opportunity, keep reading! POSITION SUMMARY The Embedded Systems Engineer will be assigned one or more systems within which replicate the avionics/aircraft systems of the land/sea/air vehicle to be simulated. These systems include communications (radios and ICS), navigation (GPS, TACAN, VOR, etc.), defensive systems (chaff, flares, ECM, etc.), offensive systems (bombs, torpedoes, missiles, etc.), aircraft systems (electrical, hydraulics, etc.), and other subsystems which may be applicable (GPWS, TCAS, IFF, etc.). ESSENTIAL DUTIES AND RESPONSIBILITIES Working in the aerospace engineering subspecialty field of avionics, Avionic engineers apply engineering principles to research, analyze, plan, design, develop, code, implement and test aircraft electrical, computer, data networks, and associated systems. Reviews and prepares engineering and technical analysis, reports, design reviews, change proposals, and other technical documentation. Applies their experience to perform functions such as simulation code development, systems integration, design, configuration management, quality assurance testing, and resource management. Acts as an engineering team member on assigned projects developing engineering procedures and controls, managing project effort, and participation in problem resolution. Interfaces with other engineers, system and program contractors, vendors, and Government representatives regarding the technical aspects of engineering programs/projects. Communicate all ongoing activities and results to other engineers, customers, stakeholders, and management in a positive manner. Read, write, and maintain new and existing software. Follows established company and industry processes and procedures. SUPERVISORY RESPONSIBILITIES This job has no supervisory responsibilities. Requirements: EXPERIENCE REQUIREMENTS Entry - 0 to 3 Years MID- 4 to 7 Years SENIOR - 7 to 10 Years The position requires developing code on Linux and Windows systems. Proficiency with C, C++, or C#. Competency with FORTRAN or Ada a plus. Competency in a scripting environment such as Python, Bash, or PowerShell. Competency in a version control system such as Git or SVN Strong technical understanding with at least one area of specialization expected ADDITIONAL REQUIRED SKILLS AND ABILITIES Ability to develop and maintain positive working relationships with internal and external customers. Ability to adapt communication style and messaging to different audiences. Ability to manage multiple priorities and projects simultaneously, ensuring stakeholder expectations are managed appropriately. Ability to work in a project-oriented, fast paced environment to meet deadlines. The candidate must be able to work in a team environment, or independently, as necessary, and be a self-starter who will attack designs and resolve problems effectively and efficiently. Proficiency in common business software (Microsoft Office - Word, Outlook, Power Point, Excel, Visio, Teams/SharePoint). Ability to work with project teams to successfully deliver new or modified functionalities. Keen attention to detail to deliver solutions that meet business requirements and are operationally supportable. EDUCATION REQUIREMENTS Bachelors or Master's in Electrical or Computer Engineering -or- Equivalent experience to education and three years' experience in related aircraft or flight simulation systems EMPLOYMENT REQUIREMENTS U.S. Citizenship Required: As a federal contractor supporting the U.S. warfighter, it is required that all employees in this role be United States citizens in compliance with government contract and security requirements. Background Check: Must be able to successfully pass an initial background screening. Security Clearance: Must be able to obtain and maintain an active Department of Defense (DoD) security clearance. The selected candidate will be subject to a government security investigation and must meet eligibility requirements for access to classified information, as required by the position. Work Schedule Flexibility: Occasional overtime and alternate shifts may be required to meet customer schedules, critical deadlines, or program milestones. Travel Requirements: Must be able and willing to travel domestically and, on occasion, internationally to customer sites, partner locations, or project meetings in support of contract objectives. Drug-Free Workplace: ASI maintains a Drug-Free Workplace. Applicants and employees are subject to pre-employment and random drug testing in accordance with company policy. Employment Contingency: All employment offers are contingent upon the successful completion of all background checks, security clearance requirements, and drug-screening results. PHYSICAL REQUIREMENTS Candidate must be able to work in small-confined spaces (simulator cockpits) Typically, employees sit comfortably to do their work, interspersed by brief periods of standing, walking, bending carrying papers and books, and extensive periods requiring the use of computer terminals to accomplish work objectives. Additional skills may be required to perform additional task(s) specific to work location, department or line of business. WORK ENVIRONMENT Work is primarily performed in an office environment consisting of offices and cubicles with low to moderate noise and bright or dim lighting. The work is fast-paced and sometimes involves extreme deadline pressures. The nature of the work requires a high degree of teamwork and cooperation with other members of the staff as well as external customers. Note: This job description is not intended to be an exhaustive list of all duties, responsibilities, or qualifications that are required of the employee for the job. Duties, responsibilities, and activities may change at any time with or without notice. The following policies apply to all areas of employment, including recruitment, hiring, training and development, promotion, transfer, termination, layoff, compensation benefits, social and recreational programs, and all other conditions and privileges of employment in accordance with applicable federal, state, and local laws. Candidate Reasonable Accommodation Candidates requiring a reasonable accommodation, as defined by the Americans with Disabilities Act, must notify Aero simulation, Inc. by e-mail at or by calling directly at . ADA Policy It is the policy of ASI to comply with all the relevant and applicable provisions of the Americans with Disabilities Act (ADA) and its Amendments. ASI will not discriminate against any qualified employee or job applicant with respect to any terms, privileges, or conditions of employment because of a person's physical or mental disability. ASI also will make reasonable accommodation wherever necessary for all employees or applicants with handicaps, disabilities, provided that the individual is otherwise qualified to safely perform the duties and assignments connected with the job and provided that any accommodations made are not an undue hardship for ASI. Equal Employment Opportunity ASI is proud to be an Equal Employment Opportunity and Affirmative Action employer dedicated to attracting, retaining, and developing a diverse and inclusive workforce. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, domestic/civil partnership or marital status . click apply for full job details
Job Description Job Description THE COMPANY For over 40 years, Aero Simulation, Inc. (ASI) has provided quality flight training devices to the US Military. ASI has successfully built numerous training systems with a primary focus on aircrew and maintenance training systems. Our current programs include, but are not limited to, the B-1 Training Systems (Air Force), E-2D Training Systems (Navy), CH-53E Training Devices (USMC), IMOMS (Coast Guard), Navigation, Seamanship, Shiphandling Trainer - NSST (Navy). To learn more about ASI visit Programs- Aero Simulation, Inc. ASI is a 100% employee-owned engineering and manufacturing company committed to supporting the nation's warfighters. Being 100% employee-owned directly impacts our culture: it drives our decision-making, motivates our teams, increases our productivity, improves retention and contributes to the future success of our company. Our culture is one where we work hard for our clients and for each other - and we have fun collaborating, sharing experience and expertise, and learning along the way. We strive to deliver exceptional quality, elevate client relationships, and enrich the careers and lives of our employee-owners. As Employee-Owners, we are invested in the success and continued development of each other and the company. In addition to the Employee Stock Ownership Plan (ESOP), we offer a flexible work environment, generous paid time off, professional development opportunities, industry competitive compensation, and superior benefits to include medical, dental, 401k and more! ASI is a great place to build a career and grow with a company that is dedicated to quality, service and to fostering a community of support for each and every member of the team. To learn more about this opportunity, keep reading! POSITION SUMMARY The Embedded Systems Engineer will be assigned one or more systems within which replicate the avionics/aircraft systems of the land/sea/air vehicle to be simulated. These systems include communications (radios and ICS), navigation (GPS, TACAN, VOR, etc.), defensive systems (chaff, flares, ECM, etc.), offensive systems (bombs, torpedoes, missiles, etc.), aircraft systems (electrical, hydraulics, etc.), and other subsystems which may be applicable (GPWS, TCAS, IFF, etc.). ESSENTIAL DUTIES AND RESPONSIBILITIES Working in the aerospace engineering subspecialty field of avionics, Avionic engineers apply engineering principles to research, analyze, plan, design, develop, code, implement and test aircraft electrical, computer, data networks, and associated systems. Reviews and prepares engineering and technical analysis, reports, design reviews, change proposals, and other technical documentation. Applies their experience to perform functions such as simulation code development, systems integration, design, configuration management, quality assurance testing, and resource management. Acts as an engineering team member on assigned projects developing engineering procedures and controls, managing project effort, and participation in problem resolution. Interfaces with other engineers, system and program contractors, vendors, and Government representatives regarding the technical aspects of engineering programs/projects. Communicate all ongoing activities and results to other engineers, customers, stakeholders, and management in a positive manner. Read, write, and maintain new and existing software. Follows established company and industry processes and procedures. SUPERVISORY RESPONSIBILITIES This job has no supervisory responsibilities. EXPERIENCE REQUIREMENTS Entry - 0 to 3 Years MID- 4 to 7 Years SENIOR - 7 to 10 Years The position requires developing code on Linux and Windows systems. Proficiency with C, C++, or C#. Competency with FORTRAN or Ada a plus. Competency in a scripting environment such as Python, Bash, or PowerShell. Competency in a version control system such as Git or SVN Strong technical understanding with at least one area of specialization expected ADDITIONAL REQUIRED SKILLS AND ABILITIES Ability to develop and maintain positive working relationships with internal and external customers. Ability to adapt communication style and messaging to different audiences. Ability to manage multiple priorities and projects simultaneously, ensuring stakeholder expectations are managed appropriately. Ability to work in a project-oriented, fast paced environment to meet deadlines. The candidate must be able to work in a team environment, or independently, as necessary, and be a self-starter who will attack designs and resolve problems effectively and efficiently. Proficiency in common business software (Microsoft Office - Word, Outlook, Power Point, Excel, Visio, Teams/SharePoint). Ability to work with project teams to successfully deliver new or modified functionalities. Keen attention to detail to deliver solutions that meet business requirements and are operationally supportable. EDUCATION REQUIREMENTS Bachelors or Master's in Electrical or Computer Engineering -or- Equivalent experience to education and three years' experience in related aircraft or flight simulation systems EMPLOYMENT REQUIREMENTS U.S. Citizenship Required: As a federal contractor supporting the U.S. warfighter, it is required that all employees in this role be United States citizens in compliance with government contract and security requirements. Background Check: Must be able to successfully pass an initial background screening. Security Clearance: Must be able to obtain and maintain an active Department of Defense (DoD) security clearance. The selected candidate will be subject to a government security investigation and must meet eligibility requirements for access to classified information, as required by the position. Work Schedule Flexibility: Occasional overtime and alternate shifts may be required to meet customer schedules, critical deadlines, or program milestones. Travel Requirements: Must be able and willing to travel domestically and, on occasion, internationally to customer sites, partner locations, or project meetings in support of contract objectives. Drug-Free Workplace: ASI maintains a Drug-Free Workplace. Applicants and employees are subject to pre-employment and random drug testing in accordance with company policy. Employment Contingency: All employment offers are contingent upon the successful completion of all background checks, security clearance requirements, and drug-screening results. PHYSICAL REQUIREMENTS Candidate must be able to work in small-confined spaces (simulator cockpits) Typically, employees sit comfortably to do their work, interspersed by brief periods of standing, walking, bending carrying papers and books, and extensive periods requiring the use of computer terminals to accomplish work objectives. Additional skills may be required to perform additional task(s) specific to work location, department or line of business. WORK ENVIRONMENT Work is primarily performed in an office environment consisting of offices and cubicles with low to moderate noise and bright or dim lighting. The work is fast-paced and sometimes involves extreme deadline pressures. The nature of the work requires a high degree of teamwork and cooperation with other members of the staff as well as external customers. Note: This job description is not intended to be an exhaustive list of all duties, responsibilities, or qualifications that are required of the employee for the job. Duties, responsibilities, and activities may change at any time with or without notice. The following policies apply to all areas of employment, including recruitment, hiring, training and development, promotion, transfer, termination, layoff, compensation benefits, social and recreational programs, and all other conditions and privileges of employment in accordance with applicable federal, state, and local laws. Candidate Reasonable Accommodation Candidates requiring a reasonable accommodation, as defined by the Americans with Disabilities Act, must notify Aero simulation, Inc. by e-mail at or by calling directly at . ADA Policy It is the policy of ASI to comply with all the relevant and applicable provisions of the Americans with Disabilities Act (ADA) and its Amendments. ASI will not discriminate against any qualified employee or job applicant with respect to any terms, privileges, or conditions of employment because of a person's physical or mental disability. ASI also will make reasonable accommodation wherever necessary for all employees or applicants with handicaps, disabilities, provided that the individual is otherwise qualified to safely perform the duties and assignments connected with the job and provided that any accommodations made are not an undue hardship for ASI. Equal Employment Opportunity ASI is proud to be an Equal Employment Opportunity and Affirmative Action employer dedicated to attracting, retaining, and developing a diverse and inclusive workforce. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, domestic/civil partnership or marital status . click apply for full job details
11/12/2026
Full time
Job Description Job Description THE COMPANY For over 40 years, Aero Simulation, Inc. (ASI) has provided quality flight training devices to the US Military. ASI has successfully built numerous training systems with a primary focus on aircrew and maintenance training systems. Our current programs include, but are not limited to, the B-1 Training Systems (Air Force), E-2D Training Systems (Navy), CH-53E Training Devices (USMC), IMOMS (Coast Guard), Navigation, Seamanship, Shiphandling Trainer - NSST (Navy). To learn more about ASI visit Programs- Aero Simulation, Inc. ASI is a 100% employee-owned engineering and manufacturing company committed to supporting the nation's warfighters. Being 100% employee-owned directly impacts our culture: it drives our decision-making, motivates our teams, increases our productivity, improves retention and contributes to the future success of our company. Our culture is one where we work hard for our clients and for each other - and we have fun collaborating, sharing experience and expertise, and learning along the way. We strive to deliver exceptional quality, elevate client relationships, and enrich the careers and lives of our employee-owners. As Employee-Owners, we are invested in the success and continued development of each other and the company. In addition to the Employee Stock Ownership Plan (ESOP), we offer a flexible work environment, generous paid time off, professional development opportunities, industry competitive compensation, and superior benefits to include medical, dental, 401k and more! ASI is a great place to build a career and grow with a company that is dedicated to quality, service and to fostering a community of support for each and every member of the team. To learn more about this opportunity, keep reading! POSITION SUMMARY The Embedded Systems Engineer will be assigned one or more systems within which replicate the avionics/aircraft systems of the land/sea/air vehicle to be simulated. These systems include communications (radios and ICS), navigation (GPS, TACAN, VOR, etc.), defensive systems (chaff, flares, ECM, etc.), offensive systems (bombs, torpedoes, missiles, etc.), aircraft systems (electrical, hydraulics, etc.), and other subsystems which may be applicable (GPWS, TCAS, IFF, etc.). ESSENTIAL DUTIES AND RESPONSIBILITIES Working in the aerospace engineering subspecialty field of avionics, Avionic engineers apply engineering principles to research, analyze, plan, design, develop, code, implement and test aircraft electrical, computer, data networks, and associated systems. Reviews and prepares engineering and technical analysis, reports, design reviews, change proposals, and other technical documentation. Applies their experience to perform functions such as simulation code development, systems integration, design, configuration management, quality assurance testing, and resource management. Acts as an engineering team member on assigned projects developing engineering procedures and controls, managing project effort, and participation in problem resolution. Interfaces with other engineers, system and program contractors, vendors, and Government representatives regarding the technical aspects of engineering programs/projects. Communicate all ongoing activities and results to other engineers, customers, stakeholders, and management in a positive manner. Read, write, and maintain new and existing software. Follows established company and industry processes and procedures. SUPERVISORY RESPONSIBILITIES This job has no supervisory responsibilities. EXPERIENCE REQUIREMENTS Entry - 0 to 3 Years MID- 4 to 7 Years SENIOR - 7 to 10 Years The position requires developing code on Linux and Windows systems. Proficiency with C, C++, or C#. Competency with FORTRAN or Ada a plus. Competency in a scripting environment such as Python, Bash, or PowerShell. Competency in a version control system such as Git or SVN Strong technical understanding with at least one area of specialization expected ADDITIONAL REQUIRED SKILLS AND ABILITIES Ability to develop and maintain positive working relationships with internal and external customers. Ability to adapt communication style and messaging to different audiences. Ability to manage multiple priorities and projects simultaneously, ensuring stakeholder expectations are managed appropriately. Ability to work in a project-oriented, fast paced environment to meet deadlines. The candidate must be able to work in a team environment, or independently, as necessary, and be a self-starter who will attack designs and resolve problems effectively and efficiently. Proficiency in common business software (Microsoft Office - Word, Outlook, Power Point, Excel, Visio, Teams/SharePoint). Ability to work with project teams to successfully deliver new or modified functionalities. Keen attention to detail to deliver solutions that meet business requirements and are operationally supportable. EDUCATION REQUIREMENTS Bachelors or Master's in Electrical or Computer Engineering -or- Equivalent experience to education and three years' experience in related aircraft or flight simulation systems EMPLOYMENT REQUIREMENTS U.S. Citizenship Required: As a federal contractor supporting the U.S. warfighter, it is required that all employees in this role be United States citizens in compliance with government contract and security requirements. Background Check: Must be able to successfully pass an initial background screening. Security Clearance: Must be able to obtain and maintain an active Department of Defense (DoD) security clearance. The selected candidate will be subject to a government security investigation and must meet eligibility requirements for access to classified information, as required by the position. Work Schedule Flexibility: Occasional overtime and alternate shifts may be required to meet customer schedules, critical deadlines, or program milestones. Travel Requirements: Must be able and willing to travel domestically and, on occasion, internationally to customer sites, partner locations, or project meetings in support of contract objectives. Drug-Free Workplace: ASI maintains a Drug-Free Workplace. Applicants and employees are subject to pre-employment and random drug testing in accordance with company policy. Employment Contingency: All employment offers are contingent upon the successful completion of all background checks, security clearance requirements, and drug-screening results. PHYSICAL REQUIREMENTS Candidate must be able to work in small-confined spaces (simulator cockpits) Typically, employees sit comfortably to do their work, interspersed by brief periods of standing, walking, bending carrying papers and books, and extensive periods requiring the use of computer terminals to accomplish work objectives. Additional skills may be required to perform additional task(s) specific to work location, department or line of business. WORK ENVIRONMENT Work is primarily performed in an office environment consisting of offices and cubicles with low to moderate noise and bright or dim lighting. The work is fast-paced and sometimes involves extreme deadline pressures. The nature of the work requires a high degree of teamwork and cooperation with other members of the staff as well as external customers. Note: This job description is not intended to be an exhaustive list of all duties, responsibilities, or qualifications that are required of the employee for the job. Duties, responsibilities, and activities may change at any time with or without notice. The following policies apply to all areas of employment, including recruitment, hiring, training and development, promotion, transfer, termination, layoff, compensation benefits, social and recreational programs, and all other conditions and privileges of employment in accordance with applicable federal, state, and local laws. Candidate Reasonable Accommodation Candidates requiring a reasonable accommodation, as defined by the Americans with Disabilities Act, must notify Aero simulation, Inc. by e-mail at or by calling directly at . ADA Policy It is the policy of ASI to comply with all the relevant and applicable provisions of the Americans with Disabilities Act (ADA) and its Amendments. ASI will not discriminate against any qualified employee or job applicant with respect to any terms, privileges, or conditions of employment because of a person's physical or mental disability. ASI also will make reasonable accommodation wherever necessary for all employees or applicants with handicaps, disabilities, provided that the individual is otherwise qualified to safely perform the duties and assignments connected with the job and provided that any accommodations made are not an undue hardship for ASI. Equal Employment Opportunity ASI is proud to be an Equal Employment Opportunity and Affirmative Action employer dedicated to attracting, retaining, and developing a diverse and inclusive workforce. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, domestic/civil partnership or marital status . click apply for full job details
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
11/12/2026
Full time
Job Description About the role DataAnnotation is looking for experienced drug discovery scientists to evaluate how frontier AI models handle real discovery and preclinical work: SAR analysis, screening cascades, DMPK and PK/PD interpretation, biomarker strategy, candidate selection. You bring the judgment you have built moving programs from target to IND. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic tasks drawn from your own practice, such as a lead optimization decision memo, a screening triage workbook, a tox package summary, or a target validation review, run them through frontier AI agents, and evaluate what comes back against a professional standard. You will work with realistic professional files, the kind a scientist in your field actually handles: experimental data, protocols, records, reports and correspondence. Some you will assemble yourself; others will be provided. In every case the goal is the same: a task a competent scientist in your field would complete correctly and a frontier model currently gets wrong. This is not a traditional lab or analysis role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth. Responsibilities Design challenging, realistic drug discovery tasks drawn from your own day to day workflows: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program review decks, correspondence), using files you author yourself or files that are provided to you. Run those tasks through frontier AI agents and evaluate the deliverable they produce (the workbook, memo, or slide deck) against the standard you would hold a colleague to. Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short. Write detailed grading rubrics that specify what a correct deliverable must contain, such as the right compounds advanced, the right liabilities flagged and the right calculations, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: misread assay data, SAR conclusions the data do not support, PK parameters misinterpreted, ignored or fabricated files, unit and scaling errors, missed safety liabilities, and off brief interpretation of the ask. Contribute across drug discovery and target biology, DMPK and preclinical safety, translational and biomarker science, biologics and advanced modalities, and CMC and process development, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on in a discovery or preclinical setting (pharma, biotech, or an academic drug discovery unit). Depth of experience in at least one of: medicinal chemistry and SAR or lead optimization; computational chemistry and CADD; assay development and screening; DMPK, PK/PD, or ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC, formulation, or analytical development. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a medicinal chemist who also understands how a screening cascade is built and how DMPK data feeds a lead optimization decision), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. Familiarity with drug discovery workflows end to end: how a program moves from target identification and validation, through hit finding and hit to lead, into lead optimization and candidate selection, and on into IND enabling studies. Nice to have: experience across more than one modality (small molecule, biologics, cell or gene therapy) or more than one therapeutic area. General requirements Master's or PhD, or a current PhD candidate, in Biology or a directly related field (molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, or computational biology), completed in the U.S., Canada, Europe, or the UK. For this role, directly related fields also include chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, and toxicology. 3+ years of hands on experience in your subfield (see Domain qualifications above). Time in an academic lab or research institute counts after undergraduate education. Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work. Hands on practitioner: you currently do (or recently did) the bench or analysis work yourself at an individual contributor level, not solely in a managerial capacity. Full professional or native level written and spoken English, with strong written communication. You can explain complex scientific reasoning clearly and concisely, and articulate why a result is wrong, not only that it is. Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the science sits partly or wholly outside your own specialization. You verify what a document claims against the underlying data. Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one. Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time). General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in life sciences professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one. Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing). Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and more hours are welcome. Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted). Compensation and terms Pay: $40 - $125/hr USD. Paid via PayPal on a regular cadence. Contract position. Fully remote. Minimum 10 hours per week with no weekly maximum. Flexible scheduling. Multi day task timers let you spread work across days. Access to Claude and ChatGPT is provided through the project; no personal subscription is required. What to expect Apply through the posting link and create your account. Complete the paid skills assessment (about 4 hours). It tests domain fit, careful reading, task design, rubric judgment and your response to feedback on a prompt. All work must be your own; submissions produced with AI tools are rejected, and this is the single most common reason candidates do not pass. Our team reviews your assessment. If you pass, you complete onboarding and a short training project that walks through how tasks, files, and rubrics are built. Propose a task from your professional experience: the scenario, the prompt, the files you will build or choose to use, and where you expect the model to fail. An expert reviewer reads it and sends written feedback either way. Once your proposal is accepted, build the task in full and run it against frontier models. Every completed task goes through expert review, and experienced contributors are invited to review and refine other experts' tasks. Each stage is a gate: work must be accepted before you move on. You are compensated for every step you complete, but not every submission is accepted. Feedback and revision are a normal part of the process. Support is available through platform instructions, onboarding materials, a dedicated Slack channel, and office hours. About DataAnnotation DataAnnotation works with frontier AI labs building the world's most advanced models, with the goal of enabling human aligned AI. Our expert programs bring together practicing professionals to evaluate and improve what these models can do in their fields. DataAnnotation believes that human expertise is essential to building trustworthy AI. We don't want AI training AI. We want real scientists in the room. If you have a strong life sciences background and want to put it to work in a new way, we'd love to hear from you.
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. The Boeing Company is seeking a Mid-Level Systems Engineer to serve as the technical lead and principal Model Based Systems Engineering (MBSE) representative for the F-15 International simulator programs in Hazelwood, Missouri. These programs include flight simulators, trainer systems, and associated subsystems. This individual will be accountable for the technical quality, integrity, and usability of the system architecture model. They will establish the model structure and modeling practices, guide and review modeling work performed by others, and help users across the team work in the model consistently. The role will provide technical leadership to junior engineers but will not have formal supervisory authority. F-15 Training Systems provides trainers to international facilities to support warfighters. These systems include high-fidelity cockpits and immersive visualization, computer-generated forces that provide synthetic opponents, and instructor applications for training setup, execution, and debrief. Work arrangement: Fully onsite in Hazelwood, Missouri. Travel is not expected, apart from occasional travel between St. Louis-area Boeing buildings. Position Responsibilities: Leads the MBSE effort for the program and serves as its primary MBSE representative to the program team and Boeing's broader MBSE community. Establishes and maintains the system architecture model, modeling approach, model structure, and practices that support the program's needs. Takes technical accountability for the model's quality and integrity, including work performed by other model contributors. Develops desktop work instructions, job aids, and guidance that help model users work consistently and avoid unintended changes to model content. Onboards and advises model users, demonstrates how to interact with the model, and reviews model contributions for technical quality and adherence to modeling practices. Provides technical leadership and mentorship to approximately one to three junior engineers. Recommends work assignments and reviews results; work assignments are made by the product owner or manager. Collaborates with MBSE leads on other programs to align modeling standards and implementation practices. Works with the Requirements Lead to support model-based requirements and traceability practices. Helps translate statement-of-work requirements into system- and subsystem-level requirements. Provides technical oversight and consultation on how model content supports verification planning; may contribute hands-on to model setup and related verification activities. Works with responsible technical personnel to document interface expectations in the model; does not own interface control document technical content. Applies an interdisciplinary, collaborative approach to plan, design, develop, and verify lifecycle-balanced systems and system solutions. Evaluates customer and operational needs to help define system performance requirements and coordinate technical parameters and program interfaces. Performs analyses to support system and system-of-systems architecture decisions and resolve cross-functional technical issues. Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology, including Manufacturing Technology, Computer Science, Data Science, Mathematics, Physics, Chemistry, or a non-U.S. equivalent qualification directly related to the work statement 3+ years of systems engineering experience Experience developing and maintaining verifiable system architecture models using SysML or a comparable modeling language Experience using requirements-management or application lifecycle management tools Experience with configuration management processes, standards and best practices Preferred Qualifications (Desired Skills/Experience): Experience with Cameo Systems Modeler. Experience establishing or maintaining MBSE modeling standards, work instructions, user guidance, or model governance practices Experience leading model reviews or guiding multiple contributors working in a shared model Experience with flight simulators, training systems, or defense simulation programs Experience with avionics, aircraft systems, or other aerospace systems Experience with software-intensive or hardware-software systems Working knowledge of INCOSE guidance and aerospace standards such as DO-178C or DO-254 Experience collaborating with engineers responsible for requirements traceability, verification, or interface documentation Conflict of Interest: Successful candidates for this job must satisfy the Company's Conflict of Interest (COI) process. Drug Free Workplace: Boeing is a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria is met as outlined in our policies. Pay & Benefits: At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent. Elements of the Total Rewards package include competitive base pay and variable compensation opportunities. The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work. The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements. Pay is based upon candidate experience and qualifications, as well as market and business considerations. Summary Pay Range: $112,200- $151,800 Applications for this position will be accepted until Nov. 25, 2026 Export Control Requirements: This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a "U.S. Person" as defined by 22 C.F.R. 120.62 is required. "U.S. Person" includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee. Export Control Details: US based job, US Person required Education Bachelor's Degree or Equivalent Required Relocation This position offers relocation based on candidate eligibility. Security Clearance This position requires the ability to obtain a U.S. Security Clearance for which the U.S. Government requires U.S. Citizenship. An interim and/or final U.S. Secret Clearance Post-Start is required. Visa Sponsorship Employer will not sponsor applicants for employment visa status. Shift This position is for 1st shift Equal Opportunity Employer: Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.
11/12/2026
Full time
Job Description At Boeing, we innovate and collaborate to make the world a better place. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us. The Boeing Company is seeking a Mid-Level Systems Engineer to serve as the technical lead and principal Model Based Systems Engineering (MBSE) representative for the F-15 International simulator programs in Hazelwood, Missouri. These programs include flight simulators, trainer systems, and associated subsystems. This individual will be accountable for the technical quality, integrity, and usability of the system architecture model. They will establish the model structure and modeling practices, guide and review modeling work performed by others, and help users across the team work in the model consistently. The role will provide technical leadership to junior engineers but will not have formal supervisory authority. F-15 Training Systems provides trainers to international facilities to support warfighters. These systems include high-fidelity cockpits and immersive visualization, computer-generated forces that provide synthetic opponents, and instructor applications for training setup, execution, and debrief. Work arrangement: Fully onsite in Hazelwood, Missouri. Travel is not expected, apart from occasional travel between St. Louis-area Boeing buildings. Position Responsibilities: Leads the MBSE effort for the program and serves as its primary MBSE representative to the program team and Boeing's broader MBSE community. Establishes and maintains the system architecture model, modeling approach, model structure, and practices that support the program's needs. Takes technical accountability for the model's quality and integrity, including work performed by other model contributors. Develops desktop work instructions, job aids, and guidance that help model users work consistently and avoid unintended changes to model content. Onboards and advises model users, demonstrates how to interact with the model, and reviews model contributions for technical quality and adherence to modeling practices. Provides technical leadership and mentorship to approximately one to three junior engineers. Recommends work assignments and reviews results; work assignments are made by the product owner or manager. Collaborates with MBSE leads on other programs to align modeling standards and implementation practices. Works with the Requirements Lead to support model-based requirements and traceability practices. Helps translate statement-of-work requirements into system- and subsystem-level requirements. Provides technical oversight and consultation on how model content supports verification planning; may contribute hands-on to model setup and related verification activities. Works with responsible technical personnel to document interface expectations in the model; does not own interface control document technical content. Applies an interdisciplinary, collaborative approach to plan, design, develop, and verify lifecycle-balanced systems and system solutions. Evaluates customer and operational needs to help define system performance requirements and coordinate technical parameters and program interfaces. Performs analyses to support system and system-of-systems architecture decisions and resolve cross-functional technical issues. Basic Qualifications (Required Skills/Experience): Bachelor of Science degree in Engineering, Engineering Technology, including Manufacturing Technology, Computer Science, Data Science, Mathematics, Physics, Chemistry, or a non-U.S. equivalent qualification directly related to the work statement 3+ years of systems engineering experience Experience developing and maintaining verifiable system architecture models using SysML or a comparable modeling language Experience using requirements-management or application lifecycle management tools Experience with configuration management processes, standards and best practices Preferred Qualifications (Desired Skills/Experience): Experience with Cameo Systems Modeler. Experience establishing or maintaining MBSE modeling standards, work instructions, user guidance, or model governance practices Experience leading model reviews or guiding multiple contributors working in a shared model Experience with flight simulators, training systems, or defense simulation programs Experience with avionics, aircraft systems, or other aerospace systems Experience with software-intensive or hardware-software systems Working knowledge of INCOSE guidance and aerospace standards such as DO-178C or DO-254 Experience collaborating with engineers responsible for requirements traceability, verification, or interface documentation Conflict of Interest: Successful candidates for this job must satisfy the Company's Conflict of Interest (COI) process. Drug Free Workplace: Boeing is a Drug Free Workplace where post offer applicants and employees are subject to testing for marijuana, cocaine, opioids, amphetamines, PCP, and alcohol when criteria is met as outlined in our policies. Pay & Benefits: At Boeing, we strive to deliver a Total Rewards package that will attract, engage and retain the top talent. Elements of the Total Rewards package include competitive base pay and variable compensation opportunities. The Boeing Company also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health insurance, flexible spending accounts, health savings accounts, retirement savings plans, life and disability insurance programs, and a number of programs that provide for both paid and unpaid time away from work. The specific programs and options available to any given employee may vary depending on eligibility factors such as geographic location, date of hire, and the applicability of collective bargaining agreements. Pay is based upon candidate experience and qualifications, as well as market and business considerations. Summary Pay Range: $112,200- $151,800 Applications for this position will be accepted until Nov. 25, 2026 Export Control Requirements: This position must meet U.S. export control compliance requirements. To meet U.S. export control compliance requirements, a "U.S. Person" as defined by 22 C.F.R. 120.62 is required. "U.S. Person" includes U.S. Citizen, U.S. National, lawful permanent resident, refugee, or asylee. Export Control Details: US based job, US Person required Education Bachelor's Degree or Equivalent Required Relocation This position offers relocation based on candidate eligibility. Security Clearance This position requires the ability to obtain a U.S. Security Clearance for which the U.S. Government requires U.S. Citizenship. An interim and/or final U.S. Secret Clearance Post-Start is required. Visa Sponsorship Employer will not sponsor applicants for employment visa status. Shift This position is for 1st shift Equal Opportunity Employer: Boeing is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law.
Job Description Job Description NextGen Security LLC Job Summary Company NextGen Security LLC Location Houston, TX Industries Security and Surveillance Job Type Full Time Employee Years of Experience 5 years of experience in the security industry Exemption Exempt System Designer What we're looking for: We are in search of a system designer with 5 years of experience within the security industry to join our fast-growing and dynamic team. What you'll be doing: This position will have responsibility for designing fully integrated security solutions which will encompass detailed parts lists, labor estimates, writing detailed scopes, generating system overview drawings and acquiring bid pricing from various subcontractor trades; this will involve site visits with Sales and sub-contractors to generate documentation as well as assisting sales in the end user proposal presentations as needed. This position will also provide software support for large integrated complex system deployments which may involve travel to the sites for the initial load and startup phases as well as being involved from a high-level perspective in oversight of the Operations department planning and standards for these systems and assisting on complex issues that may arise on customers' sites. This position will also encompass running end user training as well as train the trainer sessions either via remote conferencing or in person at the customer's sites. This role is a key position in the Sales, Engineering and Operations processes and has a direct impact on projects being able to be sold correctly, installed on budget and not only meeting our customers' expectations but also maintaining our high level of customer satisfaction. Some local travel may be required. What you bring to the table: 5 years or more of experience in the security industry. Network and IT knowledge Excellent problem solving, communication and presentation skills Ability to work independently with minimal supervision Experience with Bluebeam, Adobe Pro/Plus, Visio, MS Project and other Microsoft products Experience with AutoCAD and IP surveillance a plus. Ability to assess sites to develop requirements and define challenges. Develop project design and material list. Assist in the creation of project scope of work. Support multiple project and client deadlines and deliverables. Create design parameters to include wiring diagrams, site equipment layouts, mounting details and computer and network specifications. Assist in presenting design to customers and/or decision makers. Work with design and operations team for implementation of a project. Evaluate new industry products and re-evaluate current supported products. Keep current with training and certifications. Access Control Certifications in Genetec, Software House, ProWatch, and/or Lenel OnGuard preferred. Video Management Certifications in OnSSI, Milestone, and/or Genetec preferred. Experience in network design and implementation standards Additional Skills Must be able to communicate effectively and professionally in both written and verbal manner to customers and to those within the company. Must possess a positive attitude and be willing to interact with customers, co-workers and other personnel at all times. Commitment to integrity and to the Company Standards and Procedures. Attention to detail and accuracy is a must. What we bring to the table: Compensation based upon background and experience. Full benefits package. Vacation Cell phone allowance. We are an equal opportunity employer and drug- free workplace. Pre-employment drug screens and background checks will be conducted. Employees are subject to appropriate routine drug screens, based on job classification. Application Process Please submit your resume, references and your requested salary range when applying for this position below. More About Us NextGen Security is an electronic security systems integrator that offers commercial and industrial companies best in class industry knowledge, engineering design, implementation, management and on-going maintenance services. Our company accomplishes this by hiring only the most experienced and best-qualified talent the security industry has to offer. Management team and staff members have 10-25 years of commercial and industrial security industry expertise with single site, multi-facility, plant-wide, campus-wide, regional, national and international security projects. If that wasn't convincing enough, check out what our employees say about working at NextGen: Notice To Employment / Recruitment Agents Employment / Recruitment agents may only submit candidates for vacancies only if they have written authorization to do so from NextGen Security's HR department. Any agency candidate submission may only be submitted to positions opened to the agency through openings available via NextGen Security's website. NextGen Security will only pay a fee for candidates submitted or presented where there is a contract communication in place between the Employment / Recruitment agents and NextGen Security, and only if the candidate is submitted via approval from NextGen Security's HR department. Candidates submitted or presented by Employment / Recruitment Agents without a full approval from NextGen Security's HR department shall not be deemed to form part of any Engagement for which the Agency may claim remuneration.
11/12/2026
Full time
Job Description Job Description NextGen Security LLC Job Summary Company NextGen Security LLC Location Houston, TX Industries Security and Surveillance Job Type Full Time Employee Years of Experience 5 years of experience in the security industry Exemption Exempt System Designer What we're looking for: We are in search of a system designer with 5 years of experience within the security industry to join our fast-growing and dynamic team. What you'll be doing: This position will have responsibility for designing fully integrated security solutions which will encompass detailed parts lists, labor estimates, writing detailed scopes, generating system overview drawings and acquiring bid pricing from various subcontractor trades; this will involve site visits with Sales and sub-contractors to generate documentation as well as assisting sales in the end user proposal presentations as needed. This position will also provide software support for large integrated complex system deployments which may involve travel to the sites for the initial load and startup phases as well as being involved from a high-level perspective in oversight of the Operations department planning and standards for these systems and assisting on complex issues that may arise on customers' sites. This position will also encompass running end user training as well as train the trainer sessions either via remote conferencing or in person at the customer's sites. This role is a key position in the Sales, Engineering and Operations processes and has a direct impact on projects being able to be sold correctly, installed on budget and not only meeting our customers' expectations but also maintaining our high level of customer satisfaction. Some local travel may be required. What you bring to the table: 5 years or more of experience in the security industry. Network and IT knowledge Excellent problem solving, communication and presentation skills Ability to work independently with minimal supervision Experience with Bluebeam, Adobe Pro/Plus, Visio, MS Project and other Microsoft products Experience with AutoCAD and IP surveillance a plus. Ability to assess sites to develop requirements and define challenges. Develop project design and material list. Assist in the creation of project scope of work. Support multiple project and client deadlines and deliverables. Create design parameters to include wiring diagrams, site equipment layouts, mounting details and computer and network specifications. Assist in presenting design to customers and/or decision makers. Work with design and operations team for implementation of a project. Evaluate new industry products and re-evaluate current supported products. Keep current with training and certifications. Access Control Certifications in Genetec, Software House, ProWatch, and/or Lenel OnGuard preferred. Video Management Certifications in OnSSI, Milestone, and/or Genetec preferred. Experience in network design and implementation standards Additional Skills Must be able to communicate effectively and professionally in both written and verbal manner to customers and to those within the company. Must possess a positive attitude and be willing to interact with customers, co-workers and other personnel at all times. Commitment to integrity and to the Company Standards and Procedures. Attention to detail and accuracy is a must. What we bring to the table: Compensation based upon background and experience. Full benefits package. Vacation Cell phone allowance. We are an equal opportunity employer and drug- free workplace. Pre-employment drug screens and background checks will be conducted. Employees are subject to appropriate routine drug screens, based on job classification. Application Process Please submit your resume, references and your requested salary range when applying for this position below. More About Us NextGen Security is an electronic security systems integrator that offers commercial and industrial companies best in class industry knowledge, engineering design, implementation, management and on-going maintenance services. Our company accomplishes this by hiring only the most experienced and best-qualified talent the security industry has to offer. Management team and staff members have 10-25 years of commercial and industrial security industry expertise with single site, multi-facility, plant-wide, campus-wide, regional, national and international security projects. If that wasn't convincing enough, check out what our employees say about working at NextGen: Notice To Employment / Recruitment Agents Employment / Recruitment agents may only submit candidates for vacancies only if they have written authorization to do so from NextGen Security's HR department. Any agency candidate submission may only be submitted to positions opened to the agency through openings available via NextGen Security's website. NextGen Security will only pay a fee for candidates submitted or presented where there is a contract communication in place between the Employment / Recruitment agents and NextGen Security, and only if the candidate is submitted via approval from NextGen Security's HR department. Candidates submitted or presented by Employment / Recruitment Agents without a full approval from NextGen Security's HR department shall not be deemed to form part of any Engagement for which the Agency may claim remuneration.
