150 jobs found

Who We Are Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world's technology. What We Offer Salary: $128,000.00 - $176,000.00 Location: Benicia,CA You'll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible-while learning every day in a supportive leading global company. Visit our Careers website to learn more. At Applied Materials, we care about the health and wellbeing of our employees. We're committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits . Key Responsibilities through in-depth understanding of customer technologies and critical gaps and work throughout the work cycle until the implementation of the solution Technically leads difficult, complex application activities and team Leads the execution teams ß-sites, evaluation and demos at customers site, through high involvement in the SOW and commitment schedule Interfaces with the customer regarding technical requirements analysis and schedule Define/approve Product application support plans for installation Create & Lead new methodologies in order to optimized system performance for specific application Analysis, Develop and Transfer new application capabilities in the system envelope from product division to customer application support team Lead customer Technical review meetings Functional Knowledge Demonstrates conceptual and practical expertise in own discipline and basic knowledge of related disciplines Business Expertise Has knowledge of best practices and how own area integrates with others; is aware of the competition and the factors that differentiate them in the market Leadership Acts as a resource for colleagues with less experience; may lead small projects with manageable risks and resource requirements Problem Solving Solves complex problems; takes a new perspective on existing solutions; exercises judgment based on the analysis of multiple sources of information Impact Impacts a range of customer, operational, project or service activities within own team and other related teams; works within broad guidelines and policies Interpersonal Skills Explains difficult or sensitive information; works to build consensus Senior Applications Engineer - Semiconductor 3D X-ray Solutions As an Applications Engineer, you will join an exciting and growing team of scientists, engineers, and research professionals, focused on advancing 3D X-ray solutions for semiconductor applications. You will collaborate closely with customers and internal teams to enable cutting-edge inspection, metrology, and failure analysis workflows. Key Responsibilities Lead system demonstrations for semiconductor customers, including fabs, FA labs, and R&D centers Develop new applications and methodologies tailored to semiconductor devices, processes, and packaging technologies Provide critical input to product development by informing system specifications based on customer needs and application gaps Deliver advanced customer training on system operation, data acquisition, and analysis workflows Design, develop, and optimize robust workflows for semiconductor inspection and analysis Perform tool characterization, benchmarking, and baseline performance studies for semiconductor use cases Serve as a technical leader in customer-facing support activities, including: Delivering high-impact technical presentations and demos Supporting industry conferences and trade shows Authoring technical papers and presenting at conferences Customer Base Our customers include semiconductor manufacturing facilities (fabs), failure analysis (FA) laboratories, and advanced R&D organizations working at the forefront of device innovation. Role Impact In this role, you will act as a key technical interface between engineering, sales, and marketing, serving as the voice of the semiconductor customer. You will partner with external clients and internal development teams to: Establish best-in-class workflows for routine semiconductor inspection and analysis Identify and unlock new applications of 3D X-ray technology across front-end and back-end processes Solve high-value problems (HVPs) related to yield, reliability, process development, and advanced packaging Essential Qualifications BS or MS degree in a relevant technical field (e.g., Materials Science, Physics, Electrical Engineering, or related discipline) 3-5 years of experience in a semiconductor-related field (e.g., fabs, failure analysis, metrology, or process development) Hands-on experience designing experiments, analyzing data, and delivering results in a commercial or customer-facing environment Ability and willingness to travel up to 30% (domestic and international) Preferred Qualifications Background in microscopy or advanced imaging techniques (e.g., X-ray CT, SEM, FIB) Working knowledge of X-ray absorption principles and imaging/optical fundamentals Experience with semiconductor capital equipment, particularly in inspection, metrology, or FA workflows Familiarity with advanced semiconductor technologies (e.g., 3D IC, heterogeneous integration, advanced packaging) Additional Information Time Type: Full time Employee Type: Assignee / Regular Travel: Yes, 25% of the Time Relocation Eligible: Yes The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable. For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement. Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law. In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at , or by calling our HR Direct Help Line at , option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.

Job Description About the role DataAnnotation is looking for experienced computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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 computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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.

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care. What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you. McKesson complies with all applicable U.S. immigration laws and regulations. The Company does not provide employer support or sponsorship for any immigration related employment benefit for this role. Applicants must be currently authorized to work in the United States on a fulltime basis without the need for employer support or sponsorship now or in the future. This includes having the legal right to work in the United States without the need for McKesson support or sponsorship for any immigration related employment authorization (e.g., H1B, O1, E3, H1B1, TN, F1 OPT, F1 STEM OPT, F1 CPT, etc.) now or in the future. If you will require McKesson to provide immigration support or sponsorship now or in the future, you should not apply for this position In the Genospace team in Ontada, our mission is to be the leading information platform for applied precision medicine. Our interdisciplinary team is merging the tools and techniques of genomics, healthcare, data analytics, web development, and cloud computing. We serve research, clinical development, pathology, and clinical care customers who work with high-dimensional genomic and other biomedical data. Many of the most advanced precision medicine organizations are powered by Genospace. Description: Ontada is looking for a talented team player who wants to solve novel problems and help us build and deliver our Genospace software platform. As the leading provider of information technology services in molecular medicine, our software is used today by clinicians and researchers across the country and around the globe to improve patient care. Your Impact: Your work as a Software Engineer in the Genospace team at Ontada will impact individuals and organizations across the healthcare landscape: patients searching for the best treatment options, researchers seeking to make advances based on a sea of high-dimensional data, physicians deploying personalized medicine in everyday practice, and laboratories conducting some of the most advanced analyses in the world. What You'll Do: Work on a small team to design, develop, test, release, and support features in our web-based, cloud-hosted applications Provide feedback to your teammates through code and software design reviews, collaborating with and learning from each other while delivering great software Help shape and implement enhancements to our engineering process, products, and technology platform Minimum Requirements Typically requires 2+ years of relevant experience. Critical 2+ years of professional experience in software development Experience building web applications or data processing systems Familiarity with web technologies and a depth of experience with at least one common programming language Experience with front end tech, back-end tech and data base technology A demonstrable ability to design software to meet functional and technical requirements and to communicate your designs effectively Ability to consider problems creatively and critically while collaborating with product owners, data scientists, and designers in order to come up with solutions Additional qualifications that are helpful but not required: An interest in making an impact on healthcare and clinical research through technology Strong commitment to teamwork and a humble attitude Familiarity with the various technologies in our stack: Front End - Vue.js Back End - Java, Groovy, Grails, Kafka, Micronaut Data Tier - MongoDB, Elasticsearch A professional or academic experience in a related field including healthcare, biology, genomics, etc. Education: 4-year degree in computer science, engineering, or another STEM field or equivalent experience Physical Requirements General Office Demands We are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations. In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here. Our Base Pay Range for this position $83,400 - $139,000 McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson's (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind: McKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application. McKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates. McKesson job postings are posted on our career site: . McKesson is an Equal Opportunity Employer McKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. For additional information on McKesson's full Equal Employment Opportunity policies, visit our Equal Employment Opportunity page. McKesson is committed to being an Equal Employment Opportunity Employer and offers opportunities to all job seekers including job seekers with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, please contact us by sending an email to (United States) or (Canada) . Resumes or CVs submitted to this email box will not be accepted. Join us at McKesson!

Job Description Job Description Job Title: Data Scientist Location: Minneapolis, MN Job Summary: System One is seeking a Data Scientist with 5+ years of hands-on experience to design, build, and deploy production-grade machine learning models. In this role, you will bridge the gap between traditional data science and software engineering by writing clean, modular Python code to build robust AI capabilities embedded directly into our enterprise products and systems. Responsibilities Design, train, and validate traditional predictive and analytical machine learning models (regression, classification, decision trees, time-series, and neural networks). Write scalable, production-grade Python code to wrap models into microservices and expose them via RESTful APIs for real-time inference. Build and maintain automated data preprocessing, feature engineering, and model evaluation pipelines using CI/CD and MLOps best practices. Track model performance, data drift, and latency in production, ensuring reliability and accuracy over time. Collaborate directly with executive leadership, product directors, and enterprise architects to define the AI/ML product roadmap. Requirements 5-7+ years of progressive professional experience designing, shipping, and maintaining complex ML/AI solutions in enterprise production environments. Experience building and deploying machine learning models in a production environment. Strong practical experience with core ML algorithms (scikit-learn, XGBoost, LightGBM, statsmodels, PyTorch, or TensorFlow). Demonstrated capability in writing clean, PEP 8-compliant, modular, and object-oriented Python code (beyond Jupyter notebooks). Experience using automated testing frameworks (pytest, unittest) and writing unit/integration tests. Hands-on experience developing RESTful APIs using frameworks like FastAPI or Flask. Familiarity with containerization technologies (Docker) and version control workflows (Git, GitHub Actions, or GitLab CI/CD). Advanced SQL proficiency and experience querying structured and unstructured datasets. Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field. Preferred Qualifications: Generative AI & Enterprise LLMs: Hands-on experience developing, fine-tuning (LoRA/QLoRA), or deploying proprietary Large Language Models (LLMs), agentic frameworks, custom embeddings, or enterprise RAG systems. Experience with GPU acceleration frameworks (CUDA, TensorRT, vLLM, Ollama). Demonstrated leadership in mentoring teams and defining enterprise data and AI governance policies. System One, and its subsidiaries including Joulé and Mountain Ltd., are leaders in delivering outsourced services and workforce solutions across North America. We help clients get work done more efficiently and economically, without compromising quality. System One not only serves as a valued partner for our clients, but we offer eligible employees health and welfare benefits coverage options including medical, dental, vision, spending accounts, life insurance, voluntary plans, as well as participation in a 401(k) plan. System One is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, age, national origin, disability, family care or medical leave status, genetic information, veteran status, marital status, or any other characteristic protected by applicable federal, state, or local law. Ref: Tampa

Job Description About the role DataAnnotation is looking for experienced computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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 computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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 computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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 computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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 computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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 computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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 computational biologists and bioinformaticians to evaluate how frontier AI models handle real biological data analysis: differential expression, variant interpretation, single cell workflows, multiomics integration, statistical genetics. You bring the judgment to know when a result is real. We bring the model outputs that judgment is needed to grade. In this role, you will design challenging, realistic analysis tasks drawn from your own practice, such as a QC and interpretation exercise on an RNA seq count matrix, a variant prioritization report, a batch effect diagnosis, or a pipeline audit, 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 computational biology 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 (count matrices, sample sheets and metadata, VCFs, pipeline logs and QC output, reference annotations, analysis notebooks, 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 analysis report, annotated table, figure set, or notebook) 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 analysis must contain, such as the right normalization, the right multiple testing correction and the right biological reading, and explain in writing why a response passes or fails each one. Flag concrete failures with evidence: batch effects and confounders ignored, wrong statistical test or uncorrected p values, misused reference or annotation version, silently dropped samples, fabricated results, code that does not do what the narrative claims, and off brief interpretation of the ask. Contribute across genomics and variant interpretation, bulk and single cell transcriptomics, proteomics and multiomics, statistical and population genetics, and machine learning applied to biology, and review and refine tasks built by other experts. Domain qualifications 3+ years hands on analyzing real biological data in industry, an academic lab, or a research institute (lab or institute time counted after undergraduate education). You write, debug, and can explain your own analysis code in Python and/or R (strong proficiency in both preferred, at least one required). Depth of experience in at least one of: genomics and variant interpretation; bulk or single cell transcriptomics; proteomics, metabolomics, or multiomics integration; population and statistical genetics; machine learning applied to biology; clinical genomics; metagenomics or phylogenetics. Working understanding of several of the other areas above, enough to know what those workflows involve and how they are run (for example, a genomics specialist who also understands transcriptomics pipelines and how machine learning is applied to biological data), so you can assess work in adjacent areas and point out what was done correctly or incorrectly. You have independently owned a multistep analysis end to end, from raw or messy data, through cleaning and processing, to a final interpretation, rather than picking up a clean dataset mid pipeline. Familiar with reproducible research practices (notebooks, scripts, version control, workflow tools). You can judge whether a result is real and say what it means: normalization choices, batch effects and confounding, multiple testing correction, statistical power, and biological interpretation. 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 biostatistics, computer science, or statistics when paired with substantial biological data experience. 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.