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.
11/12/2026
Full time
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.
11/12/2026
Full time
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 Date Posted: 2026-11-04 Country: United States of America Location: US-IA-CEDAR RAPIDS-Th St NE BLDG 137 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Active and existing security clearance required after day 1 Collins Aerospace, an RTX company, is a leader in technologically advanced and intelligent solutions for the global aerospace and defense industry. Collins Aerospace has the capabilities, comprehensive portfolio, and expertise to solve customers' toughest challenges and to meet the demands of a rapidly evolving global market. The Secure Subsystems Department is responsible for the design, test, and certification of cross-domain technologies and cryptographic products that equip our warfighters to thrive in today's multi-domain battlespace. Our department collaborates with portfolio teams across the organization to help capture new business and create security-hardened solutions that address our customers' demanding needs. The High Assurance Devices Engineering team seeks a Principal Integration and Test lead with a passion for innovation and the drive to see new technology and capability matured into fielded solutions. The Integration and Test lead is a leader who is responsible for ensuring the successful verification and validation for High Assurance Devices across the department. This role will contribute to a team responsible for systems and cyber security engineering tasks related to our next-generation security-enabled mission computing, crypto, and cross-domain technologies, maturing products from low TRL through a program of record accreditation and factory production. What You Will Do Perform Formal Verification and Validation testing events such as ENV/EMI/Security Verification/TEMPEST/Design Verification Integrate and support virtual and development board test environments, combining the latest in SW virtualization with the latest in System On Chip embedded development. Collaborate with Software, Firmware, Board Support Package, Systems, and Hardware Engineers to build the next generation of Embedded Secure Systems Collaborate with Systems Architects, Product Owners, Chief Engineers, to generate Integration and Test Environments and Plans Mentor junior engineers on Integration, Validation, Verification, and Qualification. Qualifications You Must Have Experience with embedded systems development Experience with leading integration and test activities for embedded systems Experience with generating Integration and Test Plans for embedded systems and digital hardware U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance The ability to obtain and maintain a U.S. government issued security clearance is required Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 8 years prior relevant experience or an Advanced Degree in a related field and minimum 5 years of experience Qualifications We Prefer Current US Department of Defense (DoD) security clearance Experience supporting security accreditation, certification, and cybersecurity verification activities Experience with full stack software development and integration, including Board Support Package (BSP), Field Programmable Gate Array (FPGA), and Embedded Linux Experience with embedded hardware board bringup and test fixturing Experience with tools such as DOORS, CAMEO or similar. What We Offer • Medical, dental, and vision insurance • Three weeks of vacation for newly hired employees • Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option • Tuition reimbursement program • Student Loan Repayment Program • Life insurance and disability coverage • Optional coverages you can buy pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection • Birth, adoption, parental leave benefits • Ovia Health, fertility, and family planning • Adoption Assistance • Autism Benefit • Employee Assistance Plan, including up to 10 free counseling sessions • Healthy You Incentives, wellness rewards program • Doctor on Demand, virtual doctor visits • Bright Horizons, child and elder care services • Teladoc Medical Experts, second opinion program • This position is eligible for relocation assistance • And more! Learn More & Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customers succeed today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please ensure the role type (defined below) is appropriate for your needs before applying to this role. ONSITE: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. Employee Referral Eligible As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 107,500 USD - 204,500 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
11/12/2026
Full time
Job Description Date Posted: 2026-11-04 Country: United States of America Location: US-IA-CEDAR RAPIDS-Th St NE BLDG 137 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Active and existing security clearance required after day 1 Collins Aerospace, an RTX company, is a leader in technologically advanced and intelligent solutions for the global aerospace and defense industry. Collins Aerospace has the capabilities, comprehensive portfolio, and expertise to solve customers' toughest challenges and to meet the demands of a rapidly evolving global market. The Secure Subsystems Department is responsible for the design, test, and certification of cross-domain technologies and cryptographic products that equip our warfighters to thrive in today's multi-domain battlespace. Our department collaborates with portfolio teams across the organization to help capture new business and create security-hardened solutions that address our customers' demanding needs. The High Assurance Devices Engineering team seeks a Principal Integration and Test lead with a passion for innovation and the drive to see new technology and capability matured into fielded solutions. The Integration and Test lead is a leader who is responsible for ensuring the successful verification and validation for High Assurance Devices across the department. This role will contribute to a team responsible for systems and cyber security engineering tasks related to our next-generation security-enabled mission computing, crypto, and cross-domain technologies, maturing products from low TRL through a program of record accreditation and factory production. What You Will Do Perform Formal Verification and Validation testing events such as ENV/EMI/Security Verification/TEMPEST/Design Verification Integrate and support virtual and development board test environments, combining the latest in SW virtualization with the latest in System On Chip embedded development. Collaborate with Software, Firmware, Board Support Package, Systems, and Hardware Engineers to build the next generation of Embedded Secure Systems Collaborate with Systems Architects, Product Owners, Chief Engineers, to generate Integration and Test Environments and Plans Mentor junior engineers on Integration, Validation, Verification, and Qualification. Qualifications You Must Have Experience with embedded systems development Experience with leading integration and test activities for embedded systems Experience with generating Integration and Test Plans for embedded systems and digital hardware U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance The ability to obtain and maintain a U.S. government issued security clearance is required Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 8 years prior relevant experience or an Advanced Degree in a related field and minimum 5 years of experience Qualifications We Prefer Current US Department of Defense (DoD) security clearance Experience supporting security accreditation, certification, and cybersecurity verification activities Experience with full stack software development and integration, including Board Support Package (BSP), Field Programmable Gate Array (FPGA), and Embedded Linux Experience with embedded hardware board bringup and test fixturing Experience with tools such as DOORS, CAMEO or similar. What We Offer • Medical, dental, and vision insurance • Three weeks of vacation for newly hired employees • Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option • Tuition reimbursement program • Student Loan Repayment Program • Life insurance and disability coverage • Optional coverages you can buy pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection • Birth, adoption, parental leave benefits • Ovia Health, fertility, and family planning • Adoption Assistance • Autism Benefit • Employee Assistance Plan, including up to 10 free counseling sessions • Healthy You Incentives, wellness rewards program • Doctor on Demand, virtual doctor visits • Bright Horizons, child and elder care services • Teladoc Medical Experts, second opinion program • This position is eligible for relocation assistance • And more! Learn More & Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customers succeed today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please ensure the role type (defined below) is appropriate for your needs before applying to this role. ONSITE: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. Employee Referral Eligible As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 107,500 USD - 204,500 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
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.
11/12/2026
Full time
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 Date Posted: 2026-10-06 Country: United States of America Location: US-AZ-TUCSON-M E Hemisphere Loop BLDG M10 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Ability to obtain INTERIM U.S. government issued security clearance is required prior to start date At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. At Raytheon, the foundation of everything we do is rooted in our values and a higher calling - to help our nation and allies defend freedoms and deter aggression. We bring the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. Our team solves tough, meaningful problems that create a safer, more secure world. The Test Solutions Engineering (TSE) organization includes all of the engineering disciplines responsible for systems design & test of all Raytheon products. Our engineers are responsible for all elements of the development lifecycle for test including: test requirements, test system design, advanced engineering prototyping technology, flight operations, environmental testing, and environmental characterization. We are hands-on, designing with the latest technology and tools, and integrate state of the art test solutions with the most advanced engineering systems in the world. This role is 100% onsite in our Tucson, AZ Location. As a Test Systems Engineer II, you will work with multiple engineering teams to define, design, and document Test Environment capabilities and requirements. You must be a systems thinker, able to see the "big picture", and be open to growth to understand how prime hardware should be tested at every (from component to All-Up-Round) to ensure robustness and efficiency. What You Will Do You will apply your knowledge of the subsystem and collaborate with other Engineering disciplines to develop and implement the subsystem model using Model Based System Engineering tools and methods. Detail capabilities and derive requirements. Communicate test equipment needs and requirements to Test Equipment designers. Manage requirements and traceability. Leverage and apply reuse of Test Environment requirements and models to promote efficiency in test design and development. You will develop subsystem requirements, interfaces, behaviors, and architecture. You will interact with Technical SMEs as necessary to fully understand the subsystem including identifying and resolving issues and supporting related subsystem trades or analysis. Qualifications You Must Have Bachelor's in Science, Technology, Engineering, or Mathematics (STEM) with a minimum of two (2) years of experience directly related engineering experience (or a STEM Masters in lieu of experience) Systems Engineering experience in test requirements development, test planning, test techniques, test methodologies, embedded functional test, and integration, verification, and validation for test Experience demonstrating a systems engineering approach for test-related activities within the product lifecycle. The ability to obtain and maintain a US security clearance prior to the start date. U.S. citizenship is required as only U.S. citizens are eligible for a security clearance Qualifications We Prefer Experience with Agile processes and techniques. Experience with Dynamic Object-Oriented Requirements System (DOORS). Experience with development, environmental, qualification, production, manufacturing and operations testing Experience problem solving and debugging. Experience with Model Based Engineering processes, techniques, and tools (Rhapsody and IBM Rational Model Manager and/or Cameo Systems Modeler/Enterprise Architect and Teamwork Cloud preferred, with understanding of Systems Modeling Language (SysML), and/or Unified Modeling Language (UML). Experience working cross-functionally, within and outside your own team, to support the development and growth of work tasks Experience in developing, decomposing, and managing systems requirements Experience recognizing areas of improvement or customer needs and relaying them to senior staff as business development possibilities. Experience demonstrating a sense of urgency, prudent risk-taking and adaptive response to challenges. What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Clearance Information: This position requires a security clearance. DCSA Consolidated Adjudication Services (DCSA CAS), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as: Onsite Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. Candidates will learn more about role type and current site status throughout the recruiting process. Requires theoretical to advanced knowledge of work area typically obtained through University Degree combined with experience. Practical knowledge of RTX projects, programs or systems with the ability to make enhancements and leverage in daily work. Typically requires a University Degree or equivalent experience and minimum 2 years of prior relevant experience. Engineering/Other Technical Positions: Typically requires a degree in Science, Technology, Engineering or Mathematics(STEM) and a minimum of 2 years of prior relevant experience unless prohibited by local laws/regulations. As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 68,900 USD - 131,100 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: . click apply for full job details
11/12/2026
Full time
Job Description Date Posted: 2026-10-06 Country: United States of America Location: US-AZ-TUCSON-M E Hemisphere Loop BLDG M10 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Ability to obtain INTERIM U.S. government issued security clearance is required prior to start date At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedoms and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. At Raytheon, the foundation of everything we do is rooted in our values and a higher calling - to help our nation and allies defend freedoms and deter aggression. We bring the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. Our team solves tough, meaningful problems that create a safer, more secure world. The Test Solutions Engineering (TSE) organization includes all of the engineering disciplines responsible for systems design & test of all Raytheon products. Our engineers are responsible for all elements of the development lifecycle for test including: test requirements, test system design, advanced engineering prototyping technology, flight operations, environmental testing, and environmental characterization. We are hands-on, designing with the latest technology and tools, and integrate state of the art test solutions with the most advanced engineering systems in the world. This role is 100% onsite in our Tucson, AZ Location. As a Test Systems Engineer II, you will work with multiple engineering teams to define, design, and document Test Environment capabilities and requirements. You must be a systems thinker, able to see the "big picture", and be open to growth to understand how prime hardware should be tested at every (from component to All-Up-Round) to ensure robustness and efficiency. What You Will Do You will apply your knowledge of the subsystem and collaborate with other Engineering disciplines to develop and implement the subsystem model using Model Based System Engineering tools and methods. Detail capabilities and derive requirements. Communicate test equipment needs and requirements to Test Equipment designers. Manage requirements and traceability. Leverage and apply reuse of Test Environment requirements and models to promote efficiency in test design and development. You will develop subsystem requirements, interfaces, behaviors, and architecture. You will interact with Technical SMEs as necessary to fully understand the subsystem including identifying and resolving issues and supporting related subsystem trades or analysis. Qualifications You Must Have Bachelor's in Science, Technology, Engineering, or Mathematics (STEM) with a minimum of two (2) years of experience directly related engineering experience (or a STEM Masters in lieu of experience) Systems Engineering experience in test requirements development, test planning, test techniques, test methodologies, embedded functional test, and integration, verification, and validation for test Experience demonstrating a systems engineering approach for test-related activities within the product lifecycle. The ability to obtain and maintain a US security clearance prior to the start date. U.S. citizenship is required as only U.S. citizens are eligible for a security clearance Qualifications We Prefer Experience with Agile processes and techniques. Experience with Dynamic Object-Oriented Requirements System (DOORS). Experience with development, environmental, qualification, production, manufacturing and operations testing Experience problem solving and debugging. Experience with Model Based Engineering processes, techniques, and tools (Rhapsody and IBM Rational Model Manager and/or Cameo Systems Modeler/Enterprise Architect and Teamwork Cloud preferred, with understanding of Systems Modeling Language (SysML), and/or Unified Modeling Language (UML). Experience working cross-functionally, within and outside your own team, to support the development and growth of work tasks Experience in developing, decomposing, and managing systems requirements Experience recognizing areas of improvement or customer needs and relaying them to senior staff as business development possibilities. Experience demonstrating a sense of urgency, prudent risk-taking and adaptive response to challenges. What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Clearance Information: This position requires a security clearance. DCSA Consolidated Adjudication Services (DCSA CAS), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: Please ensure the role type defined below is appropriate for your needs before applying to this role. This position is classified as: Onsite Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. Candidates will learn more about role type and current site status throughout the recruiting process. Requires theoretical to advanced knowledge of work area typically obtained through University Degree combined with experience. Practical knowledge of RTX projects, programs or systems with the ability to make enhancements and leverage in daily work. Typically requires a University Degree or equivalent experience and minimum 2 years of prior relevant experience. Engineering/Other Technical Positions: Typically requires a degree in Science, Technology, Engineering or Mathematics(STEM) and a minimum of 2 years of prior relevant experience unless prohibited by local laws/regulations. As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 68,900 USD - 131,100 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: . click apply for full job details
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.
11/12/2026
Full time
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.
11/12/2026
Full time
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.
11/12/2026
Full time
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 Job Description About Methods Methods Machine Tools supplies high-quality, high-precision machine tools and automation solutions, and designs integrated custom machining processes and systems. Headquartered in Sudbury, Mass., and in operation since 1958, Methods provides an end-to-end portfolio of best-in-class products, engineering, service, parts, training, and integration. Methods has more than 300 employees, seven technology centers, one high precision machining center, and has installed in excess of 45,000 machines throughout North America. POSITION OVERVIEW This position is responsible for supporting the application, setup, programming, and operation of CNC machines. The ideal candidate will have expertise in CNC programming, machining processes, and the ability to support customers with technical issues. Position requires occasional overnight travel. Benefits: Medical, Dental, Vision, Paid time off, Education Assistance, 401(k) Plan, and great opportunity for career growth. Key Responsibilities Technical Support: Provide technical application support to customers, both remotely and on-site, ensuring CNC machines are running efficiently. Machine Setup & Operation: Assist in the initial setup and configuration of CNC machines, ensuring optimal machine performance. Programming & Troubleshooting: Develop, modify, and optimize CNC programs. Troubleshoot and resolve any issues related to machine operations, including software and hardware challenges. Customer Training: Train customers on CNC machine operations, programming techniques, and machine maintenance to maximize productivity and ensure safety. Documentation: Create and maintain detailed documentation for programming, procedures, and troubleshooting steps. Product Testing: Conduct tests and demonstrations of new CNC systems, providing feedback to engineering and product development teams. Collaboration: Work closely with sales teams to tailor CNC solutions to customer needs. Continuous Improvement: Stay up-to-date with advancements in CNC technology, integrating new features and methods to improve efficiency. required skills/abilities Skills: Proficiency with CNC programming languages (e.g., G-code, M-code, FANUC) Strong knowledge of CNC machine types (e.g., mills, lathes, multi-axis machines). Understanding of machining processes, cutting tools, and materials. Experience in troubleshooting and resolving technical issues with CNC equipment. Excellent problem-solving and analytical skills. Strong communication skills for customer support and internal collaboration. Ability to train and mentor staff or customers effectively. Experience with CAD/CAM software Familiarity with automation and robotics in CNC manufacturing environments. Project management experience is an advantage. minimum education and experience Bachelor's degree in Mechanical Engineering, Manufacturing Engineering, Industrial Engineering, or related field. Relevant certifications and training in CNC technologies are a plus. Experience: Minimum of 10 years of hands-on experience with CNC machine operation, programming, and applications in manufacturing or production environments. Additional Information This job description is designed to provide an overview of basic skills, experiences and education required to perform the job and is not designed to cover or contain a comprehensive listing of all activities, duties, or responsibilities required of the employee to perform the essential functions of the job. In instances where educational degrees are required, the company will take into consideration factors such as additional experience, training, or certifications in lieu of specific educational requirements .
11/12/2026
Full time
Job Description Job Description About Methods Methods Machine Tools supplies high-quality, high-precision machine tools and automation solutions, and designs integrated custom machining processes and systems. Headquartered in Sudbury, Mass., and in operation since 1958, Methods provides an end-to-end portfolio of best-in-class products, engineering, service, parts, training, and integration. Methods has more than 300 employees, seven technology centers, one high precision machining center, and has installed in excess of 45,000 machines throughout North America. POSITION OVERVIEW This position is responsible for supporting the application, setup, programming, and operation of CNC machines. The ideal candidate will have expertise in CNC programming, machining processes, and the ability to support customers with technical issues. Position requires occasional overnight travel. Benefits: Medical, Dental, Vision, Paid time off, Education Assistance, 401(k) Plan, and great opportunity for career growth. Key Responsibilities Technical Support: Provide technical application support to customers, both remotely and on-site, ensuring CNC machines are running efficiently. Machine Setup & Operation: Assist in the initial setup and configuration of CNC machines, ensuring optimal machine performance. Programming & Troubleshooting: Develop, modify, and optimize CNC programs. Troubleshoot and resolve any issues related to machine operations, including software and hardware challenges. Customer Training: Train customers on CNC machine operations, programming techniques, and machine maintenance to maximize productivity and ensure safety. Documentation: Create and maintain detailed documentation for programming, procedures, and troubleshooting steps. Product Testing: Conduct tests and demonstrations of new CNC systems, providing feedback to engineering and product development teams. Collaboration: Work closely with sales teams to tailor CNC solutions to customer needs. Continuous Improvement: Stay up-to-date with advancements in CNC technology, integrating new features and methods to improve efficiency. required skills/abilities Skills: Proficiency with CNC programming languages (e.g., G-code, M-code, FANUC) Strong knowledge of CNC machine types (e.g., mills, lathes, multi-axis machines). Understanding of machining processes, cutting tools, and materials. Experience in troubleshooting and resolving technical issues with CNC equipment. Excellent problem-solving and analytical skills. Strong communication skills for customer support and internal collaboration. Ability to train and mentor staff or customers effectively. Experience with CAD/CAM software Familiarity with automation and robotics in CNC manufacturing environments. Project management experience is an advantage. minimum education and experience Bachelor's degree in Mechanical Engineering, Manufacturing Engineering, Industrial Engineering, or related field. Relevant certifications and training in CNC technologies are a plus. Experience: Minimum of 10 years of hands-on experience with CNC machine operation, programming, and applications in manufacturing or production environments. Additional Information This job description is designed to provide an overview of basic skills, experiences and education required to perform the job and is not designed to cover or contain a comprehensive listing of all activities, duties, or responsibilities required of the employee to perform the essential functions of the job. In instances where educational degrees are required, the company will take into consideration factors such as additional experience, training, or certifications in lieu of specific educational requirements .
Job Description Job Description Premier Global Links LLC is seeking an experienced Senior Backend Engineer to join a fast-growing technology team building next-generation AI-powered business intelligence solutions. This role requires strong backend engineering experience, expertise in scalable data systems, and the ability to work closely with engineering, product, leadership, and customers in a fast-paced environment. Key Responsibilities Build and scale large-scale data pipelines for latency-sensitive analytical workloads. Design and develop secure, cost-efficient, and highly scalable backend systems. Work across the product and codebase in a hands-on engineering environment. Develop and maintain backend applications using Python and TypeScript . Collaborate with engineering teams, product leaders, company founders, and customers. Contribute to product strategy, technical architecture, and engineering roadmap decisions. Help establish engineering practices, technology standards, and scalable technical infrastructure. Solve complex backend and data engineering challenges as the platform continues to grow. Required Qualifications Bachelor's degree in Computer Science, Electrical Engineering, Physics, Statistics, Mathematics, or a related technical field. 4+ years of professional backend engineering experience. Experience in product, platform, or infrastructure engineering. Experience building or working with large-scale data pipelines . Strong proficiency in Python and TypeScript . Strong software engineering, system design, and problem-solving skills. Experience working in a strong engineering-focused technology environment. Strong communication and collaboration skills. U.S. work authorization. Ability to work in the Bay Area or relocate to the San Mateo area. Preferred Qualifications Experience working at a fast-growing startup or technology company. Experience with scalable cloud infrastructure and distributed systems. Experience building data-intensive, analytical, or AI-powered products. Experience collaborating directly with product teams, technical leadership, and customers. Strong academic background in a relevant technical discipline. Work Schedule Hybrid: Minimum 4 days in the office in San Mateo, CA, with the remaining workday remote. The role is designed for a highly collaborative engineering environment where team members work closely with product and technical leadership. Compensation $250,000-$300,000 per year + competitive equity. Visa sponsorship may be considered on a case-by-case basis, including TN visas and eligible H-1B transfers. Premier Global Links LLC is an Equal Opportunity Employer. Qualified applicants will be considered without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other status protected by applicable law.
11/12/2026
Full time
Job Description Job Description Premier Global Links LLC is seeking an experienced Senior Backend Engineer to join a fast-growing technology team building next-generation AI-powered business intelligence solutions. This role requires strong backend engineering experience, expertise in scalable data systems, and the ability to work closely with engineering, product, leadership, and customers in a fast-paced environment. Key Responsibilities Build and scale large-scale data pipelines for latency-sensitive analytical workloads. Design and develop secure, cost-efficient, and highly scalable backend systems. Work across the product and codebase in a hands-on engineering environment. Develop and maintain backend applications using Python and TypeScript . Collaborate with engineering teams, product leaders, company founders, and customers. Contribute to product strategy, technical architecture, and engineering roadmap decisions. Help establish engineering practices, technology standards, and scalable technical infrastructure. Solve complex backend and data engineering challenges as the platform continues to grow. Required Qualifications Bachelor's degree in Computer Science, Electrical Engineering, Physics, Statistics, Mathematics, or a related technical field. 4+ years of professional backend engineering experience. Experience in product, platform, or infrastructure engineering. Experience building or working with large-scale data pipelines . Strong proficiency in Python and TypeScript . Strong software engineering, system design, and problem-solving skills. Experience working in a strong engineering-focused technology environment. Strong communication and collaboration skills. U.S. work authorization. Ability to work in the Bay Area or relocate to the San Mateo area. Preferred Qualifications Experience working at a fast-growing startup or technology company. Experience with scalable cloud infrastructure and distributed systems. Experience building data-intensive, analytical, or AI-powered products. Experience collaborating directly with product teams, technical leadership, and customers. Strong academic background in a relevant technical discipline. Work Schedule Hybrid: Minimum 4 days in the office in San Mateo, CA, with the remaining workday remote. The role is designed for a highly collaborative engineering environment where team members work closely with product and technical leadership. Compensation $250,000-$300,000 per year + competitive equity. Visa sponsorship may be considered on a case-by-case basis, including TN visas and eligible H-1B transfers. Premier Global Links LLC is an Equal Opportunity Employer. Qualified applicants will be considered without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other status protected by applicable law.
Job Description Date Posted: 2026-08-04 Country: United States of America Location: US-CA-FULLERTON- Hughes Dr BLDG 676 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Active and existing security clearance required after day 1 Raytheon Company, Managed by Collins Aerospace At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Collins Aerospace, an RTX company, is a leader in technologically advanced, intelligent solutions that help redefine the aerospace and defense industry. With a comprehensive portfolio and deep technical expertise, we help customers meet the demands of the global market. Join us and help shape the future of aerospace and defense. We are looking for Principal Systems Engineers in Algorithms to support the Joint Precision Approach and Landing System (JPALS) program. This individual will be required to run and test modeling and simulation of algorithmic models using Simulink and MATLAB. The work is highly technical, and the individual must be comfortable with documenting equations, derivation, and generation of plots in MATLAB. The individual must be comfortable with data and statistical analysis. The successful candidate must be able to work on individual tasks under limited supervision, or with a team to achieve operational targets and must have the ability to communicate and collaborate with parties within and outside of own job function. Eventually the candidate may be responsible for helping to provide guidance, coaching and training to other employees within their job area. What You Will Do • Develop algorithms, tools and models for the JPALS program • Provide guidance to the team to conduct analysis for system trouble shooting and analysis • Conduct independent research and analysis • Draft technical papers and algorithm descriptions • Lead technical discussion with customers and team Sign-on Bonus Eligible Qualifications You Must Have • Proficient in MATLAB; code optimization and numerical stability • Proficient in basic software development principles - configuration control, collaborative development, formal processes • Proficient in reading and developing algorithm descriptions, mathematical expressions and derivations, matrix manipulations, outlier analysis, and system level analysis • Proficient in performing data analysis and problem solving (e.g. anomaly investigation)Proficient technical writing and oral communication • Experience performing model driven design using Simulink and auto generated code • Understands the systems engineering discipline • Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 8 years prior relevant experience or an Advanced Degree in a related field and minimum 5 years of experience Qualifications We Prefer • Experience performing complex statistical analysis and/or mathematical modeling • Experience with development of solutions to GPS Navigation Systems and Global Navigation Satellite Systems (GNSS) theory • Familiar with requirement development and sell off • Willingness to travel and do field work What We Offer • Medical, dental, and vision insurance • Three weeks of vacation for newly hired employees • Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option • Tuition reimbursement program • Student Loan Repayment Program • Life insurance and disability coverage • Optional coverages you can buy pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection • Birth, adoption, parental leave benefits • Ovia Health, fertility, and family planning • Adoption Assistance • Autism Benefit • Employee Assistance Plan, including up to 10 free counseling sessions • Healthy You Incentives, wellness rewards program • Doctor on Demand, virtual doctor visits • Bright Horizons, child and elder care services • Teladoc Medical Experts, second opinion program • And more! Learn More & Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customer succeeds today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please ensure the role type (defined below) is appropriate for your needs before applying to this role. ONSITE: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. Employee Referral Eligible As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 118,300 USD - 224,900 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
11/12/2026
Full time
Job Description Date Posted: 2026-08-04 Country: United States of America Location: US-CA-FULLERTON- Hughes Dr BLDG 676 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Active and existing security clearance required after day 1 Raytheon Company, Managed by Collins Aerospace At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Collins Aerospace, an RTX company, is a leader in technologically advanced, intelligent solutions that help redefine the aerospace and defense industry. With a comprehensive portfolio and deep technical expertise, we help customers meet the demands of the global market. Join us and help shape the future of aerospace and defense. We are looking for Principal Systems Engineers in Algorithms to support the Joint Precision Approach and Landing System (JPALS) program. This individual will be required to run and test modeling and simulation of algorithmic models using Simulink and MATLAB. The work is highly technical, and the individual must be comfortable with documenting equations, derivation, and generation of plots in MATLAB. The individual must be comfortable with data and statistical analysis. The successful candidate must be able to work on individual tasks under limited supervision, or with a team to achieve operational targets and must have the ability to communicate and collaborate with parties within and outside of own job function. Eventually the candidate may be responsible for helping to provide guidance, coaching and training to other employees within their job area. What You Will Do • Develop algorithms, tools and models for the JPALS program • Provide guidance to the team to conduct analysis for system trouble shooting and analysis • Conduct independent research and analysis • Draft technical papers and algorithm descriptions • Lead technical discussion with customers and team Sign-on Bonus Eligible Qualifications You Must Have • Proficient in MATLAB; code optimization and numerical stability • Proficient in basic software development principles - configuration control, collaborative development, formal processes • Proficient in reading and developing algorithm descriptions, mathematical expressions and derivations, matrix manipulations, outlier analysis, and system level analysis • Proficient in performing data analysis and problem solving (e.g. anomaly investigation)Proficient technical writing and oral communication • Experience performing model driven design using Simulink and auto generated code • Understands the systems engineering discipline • Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 8 years prior relevant experience or an Advanced Degree in a related field and minimum 5 years of experience Qualifications We Prefer • Experience performing complex statistical analysis and/or mathematical modeling • Experience with development of solutions to GPS Navigation Systems and Global Navigation Satellite Systems (GNSS) theory • Familiar with requirement development and sell off • Willingness to travel and do field work What We Offer • Medical, dental, and vision insurance • Three weeks of vacation for newly hired employees • Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option • Tuition reimbursement program • Student Loan Repayment Program • Life insurance and disability coverage • Optional coverages you can buy pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection • Birth, adoption, parental leave benefits • Ovia Health, fertility, and family planning • Adoption Assistance • Autism Benefit • Employee Assistance Plan, including up to 10 free counseling sessions • Healthy You Incentives, wellness rewards program • Doctor on Demand, virtual doctor visits • Bright Horizons, child and elder care services • Teladoc Medical Experts, second opinion program • And more! Learn More & Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customer succeeds today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please ensure the role type (defined below) is appropriate for your needs before applying to this role. ONSITE: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. Employee Referral Eligible As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 118,300 USD - 224,900 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
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.
11/12/2026
Full time
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 Job Description About the job Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey . Position: Systems & Integration Engineering Experts Type: Contract Compensation: $100-$120/hour Location: Remote Role Responsibilities Apply hands-on systems , integration , reliability , and test engineering expertise to solve complex problems involving real physical products and hardware systems. Review and evaluate system requirements, interfaces, integration plans, verification strategies, test procedures, qualification approaches, reliability analyses, and manufacturing test workflows . Analyze system-level interactions across electrical, mechanical, firmware/software, thermal, controls, and manufacturing domains . Diagnose integration failures and identify root causes, interface issues, design deficiencies, reliability risks, manufacturing defects, and test coverage gaps . Develop or review technically rigorous engineering problems, reference solutions, test cases, verification criteria, failure analyses, and technical documentation. Provide clear technical feedback grounded in real-world hardware development, testing, and production experience. Qualifications Must-Have 5+ years of professional systems , integration , reliability , or test engineering experience, including at least 3 years of recent hands-on experience with hardware integration, test, validation, qualification, failure analysis, manufacturing test, or troubleshooting. Demonstrated experience as the responsible engineer for complex physical systems or hardware products. Deep technical expertise in at least one core domain: systems engineering and requirements/V&V hardware integration and test; reliability engineering and failure analysis; or manufacturing/production test engineering . Strong practical command of requirements traceability, interface management, verification and validation, test planning, FMEA/FMECA, fault isolation, root-cause analysis, reliability, qualification, environmental testing, design-for-test, manufacturing yield, and system-level failure modes . Extensive hands-on experience integrating, instrumenting, testing, debugging, and characterizing physical hardware . Preferred Experience working for a company that designs, builds, integrates, tests, and manufactures complex physical hardware systems . Application Process (Takes 20-30 mins to complete) Upload resume AI interview based on your resume Submit form Resources & Support For details about the interview process and platform information, please check: For any help or support, reach out to: PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
11/12/2026
Full time
Job Description Job Description About the job Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey . Position: Systems & Integration Engineering Experts Type: Contract Compensation: $100-$120/hour Location: Remote Role Responsibilities Apply hands-on systems , integration , reliability , and test engineering expertise to solve complex problems involving real physical products and hardware systems. Review and evaluate system requirements, interfaces, integration plans, verification strategies, test procedures, qualification approaches, reliability analyses, and manufacturing test workflows . Analyze system-level interactions across electrical, mechanical, firmware/software, thermal, controls, and manufacturing domains . Diagnose integration failures and identify root causes, interface issues, design deficiencies, reliability risks, manufacturing defects, and test coverage gaps . Develop or review technically rigorous engineering problems, reference solutions, test cases, verification criteria, failure analyses, and technical documentation. Provide clear technical feedback grounded in real-world hardware development, testing, and production experience. Qualifications Must-Have 5+ years of professional systems , integration , reliability , or test engineering experience, including at least 3 years of recent hands-on experience with hardware integration, test, validation, qualification, failure analysis, manufacturing test, or troubleshooting. Demonstrated experience as the responsible engineer for complex physical systems or hardware products. Deep technical expertise in at least one core domain: systems engineering and requirements/V&V hardware integration and test; reliability engineering and failure analysis; or manufacturing/production test engineering . Strong practical command of requirements traceability, interface management, verification and validation, test planning, FMEA/FMECA, fault isolation, root-cause analysis, reliability, qualification, environmental testing, design-for-test, manufacturing yield, and system-level failure modes . Extensive hands-on experience integrating, instrumenting, testing, debugging, and characterizing physical hardware . Preferred Experience working for a company that designs, builds, integrates, tests, and manufactures complex physical hardware systems . Application Process (Takes 20-30 mins to complete) Upload resume AI interview based on your resume Submit form Resources & Support For details about the interview process and platform information, please check: For any help or support, reach out to: PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.
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.
11/12/2026
Full time
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 Date Posted: 2026-11-03 Country: United States of America Location: US-AZ-TUCSON- E Hermans Rd BLDG 805 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Ability to obtain INTERIM U.S. government issued security clearance is required prior to start date At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedom and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. Raytheon's Systems Engineering & Test Capabilities (SETC) Centers in Tucson, AZ support technologies spanning the seabed to space, including ship-launched systems such as Standard Missile and Tomahawk, surface-launched systems including Javelin and Coyote, air-launched systems such as AMRAAM and StormBreaker, and emerging technologies including hypersonic and space solutions. The Processing, Guidance, & Controls (PG&C) Center provides end to end capabilities that enable precise guidance, robust sensing, and high fidelity product understanding. The following 3 Engineering teams are all part of the Processing, Guidance, & Controls Center: Guidance, Navigation, and Control - develops aerodynamic models, autopilot designs, optimized motion strategies, and state estimation to meet mission objectives. Signal Processing - extracts and interprets information from active and passive sensors to detect, track, and classify objects of interest. Modeling, Simulation, and Analysis - delivers product level simulations that represent system components, subsystems, and operating environments, supporting the design, development, verification, and validation of our products and technologies. The PG&C Center in Tucson, AZ are currently hiring Systems Engineer II - Processing, Guidance, & Controls Engineers to support the execution of our current and upcoming product lines. These roles are 100% on-site at our Tucson, AZ campus with the possibility of occasional travel. What you will do: Develop, simulate, and evaluate control and navigation algorithms for complex, autonomous systems; apply model-based design, simulation, and test data to improve system performance. Build and use dynamic system models and simulations to evaluate system behavior, conduct design trades and reduce development test cycles. Analyze test data to evaluate system performance, identify issues, and drive engineering improvements. Work cross-functionally with mechanical, electrical, software, systems, and test teams to integrate and validate complex systems. Experience developing algorithms, system models, simulation tools, controls, signal processing or performance-analysis solutions for complex products. Qualifications You Must Have Typically requires a degree in Engineering, Science, Physics, Mathematics, or a related STEM degree and a minimum of 2 years prior relevant experience Engineering experience in at least one of the following areas: Guidance, navigation, and control (GNC) systems Navigation sensors, state estimation, and/or perception systems Guidance and control algorithms System modeling and simulation Parameter tuning and validation Test data and/or data logging analysis and system performance Root-cause analysis, debugging, trouble shooting, and performance analysis Real-time guidance, navigation, and control software Integrated guidance and control system development Mission performance (accuracy, range, lethality, reliability Experience with MATLAB, Python, C, C++, and/or Object-Oriented design Ability to obtain an Interim Secret U.S. government issued Security Clearance is required prior to start date with the ability to obtain special program access after start. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance. Qualifications We Prefer Experience working with multidisciplinary engineering teams; such as: systems, software, sensors, electrical, mechanical, and/or test engineering Advanced Engineering Degree What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Relocation Eligible - relocation assistance is available for this position Learn More & Apply Now! Please consider the following role type definition as you apply for this role. Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. This position requires onsite work in Tucson, AZ: -tucson,-az-location As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 75,800 USD - 144,200 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
11/12/2026
Full time
Job Description Date Posted: 2026-11-03 Country: United States of America Location: US-AZ-TUCSON- E Hermans Rd BLDG 805 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Ability to obtain INTERIM U.S. government issued security clearance is required prior to start date At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Raytheon brings the strength of more than 100 years of experience and renowned engineering expertise to meet the needs of today's mission and stay ahead of tomorrow's threat. We deliver solutions that help our nation and allies defend freedom and deter aggression, creating a safer, more secure world. Join us and help shape the future of aerospace and defense. Raytheon's Systems Engineering & Test Capabilities (SETC) Centers in Tucson, AZ support technologies spanning the seabed to space, including ship-launched systems such as Standard Missile and Tomahawk, surface-launched systems including Javelin and Coyote, air-launched systems such as AMRAAM and StormBreaker, and emerging technologies including hypersonic and space solutions. The Processing, Guidance, & Controls (PG&C) Center provides end to end capabilities that enable precise guidance, robust sensing, and high fidelity product understanding. The following 3 Engineering teams are all part of the Processing, Guidance, & Controls Center: Guidance, Navigation, and Control - develops aerodynamic models, autopilot designs, optimized motion strategies, and state estimation to meet mission objectives. Signal Processing - extracts and interprets information from active and passive sensors to detect, track, and classify objects of interest. Modeling, Simulation, and Analysis - delivers product level simulations that represent system components, subsystems, and operating environments, supporting the design, development, verification, and validation of our products and technologies. The PG&C Center in Tucson, AZ are currently hiring Systems Engineer II - Processing, Guidance, & Controls Engineers to support the execution of our current and upcoming product lines. These roles are 100% on-site at our Tucson, AZ campus with the possibility of occasional travel. What you will do: Develop, simulate, and evaluate control and navigation algorithms for complex, autonomous systems; apply model-based design, simulation, and test data to improve system performance. Build and use dynamic system models and simulations to evaluate system behavior, conduct design trades and reduce development test cycles. Analyze test data to evaluate system performance, identify issues, and drive engineering improvements. Work cross-functionally with mechanical, electrical, software, systems, and test teams to integrate and validate complex systems. Experience developing algorithms, system models, simulation tools, controls, signal processing or performance-analysis solutions for complex products. Qualifications You Must Have Typically requires a degree in Engineering, Science, Physics, Mathematics, or a related STEM degree and a minimum of 2 years prior relevant experience Engineering experience in at least one of the following areas: Guidance, navigation, and control (GNC) systems Navigation sensors, state estimation, and/or perception systems Guidance and control algorithms System modeling and simulation Parameter tuning and validation Test data and/or data logging analysis and system performance Root-cause analysis, debugging, trouble shooting, and performance analysis Real-time guidance, navigation, and control software Integrated guidance and control system development Mission performance (accuracy, range, lethality, reliability Experience with MATLAB, Python, C, C++, and/or Object-Oriented design Ability to obtain an Interim Secret U.S. government issued Security Clearance is required prior to start date with the ability to obtain special program access after start. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance. Qualifications We Prefer Experience working with multidisciplinary engineering teams; such as: systems, software, sensors, electrical, mechanical, and/or test engineering Advanced Engineering Degree What We Offer Our values drive our actions, behaviors, and performance with a vision for a safer, more connected world. At RTX we value: Safety, Trust, Respect, Accountability, Collaboration, and Innovation. Relocation Eligible - relocation assistance is available for this position Learn More & Apply Now! Please consider the following role type definition as you apply for this role. Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. This position requires onsite work in Tucson, AZ: -tucson,-az-location As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 75,800 USD - 144,200 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
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.
11/12/2026
Full time
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 Date Posted: 2026-11-03 Country: United States of America Location: US-IA-CEDAR RAPIDS- Collins Rd NE BLDG 121 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Active and existing security clearance required after day 1 Collins Aerospace, an RTX company, is a leader in technologically advanced and intelligent solutions for the global aerospace and defense industry. Collins Aerospace has the capabilities, comprehensive portfolio, and expertise to solve customers' toughest challenges and to meet the demands of a rapidly evolving global market. Collins Aerospace is seeking a Senior Technical Project Manager - Tactical Communications to join our team in Cedar Rapids, IA. We're looking for a driven and experienced Technical Project Manager to lead a cross-functional Integrated Product Team focused on developing next-generation Tactical Communications. This role plays a key part in advancing technologies from early-stage concepts (low TRL) through accreditation and full-scale production. As part of our Systems Engineering team, you'll work on cutting-edge solutions that support critical programs. We value innovation, collaboration, and continuous learning. If you're a creative problem-solver with a passion for technology and a desire to make a real impact, we want to hear from you. Join us and help shape the future of secure systems! What You Will Do Work closely with a highly motivated and dedicated team of professionals while managing product development efforts, helping to provide solutions to our customers. Interact and interface with External Customers Participate in the systems development lifecycle, including trade study execution, system design, requirements definition, requirements allocation, requirement traceability, systems integration, verification testing and qualification. Manage Program Schedule and Cost Performance (SPI / CPI) for small to medium sized programs, and help generate the Integrated Master Schedule (IMS) Collaborate across many engineering disciplines to execute program milestones Facilitate customer demonstration events and program reviews both onsite and offsite Lead bid and proposal efforts Present technical and program data to customers and executive leadership Ability to travel up to 10% Sign-on Bonus Eligible Qualifications You Must Have Experience with the systems development process, including requirements capture, design, decomposition, integration, and verification/validation. Experience working in an Agile environment. The ability to obtain and maintain a U.S. government issued Secret security clearance is required . U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 5 years prior relevant experience or an Advanced Degree in a related field and minimum 3 years of experience Qualifications We Prefer Experience working in an Agile environment. Proven leadership of large-scale programs throughout the full development lifecycle, with strong proficiency in project management methodologies (e.g., Waterfall, SAFe Agile) and financial tools such as Earned Value Management (EVM). Exceptionally strong communication skills, experience presenting technical data to program management and executive leadership. Prior experience in a defense or aerospace engineering environment. What We Offer • Medical, dental, and vision insurance • Three weeks of vacation for newly hired employees • Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option • Tuition reimbursement program • Student Loan Repayment Program • Life insurance and disability coverage • Optional coverages you can buy pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection • Birth, adoption, parental leave benefits • Ovia Health, fertility, and family planning • Adoption Assistance • Autism Benefit • Employee Assistance Plan, including up to 10 free counseling sessions • Healthy You Incentives, wellness rewards program • Doctor on Demand, virtual doctor visits • Bright Horizons, child and elder care services • Teladoc Medical Experts, second opinion program • This position is eligible for relocation assistance • And more! Learn More & Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customers succeed today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please ensure the role type (defined below) is appropriate for your needs before applying to this role. ONSITE: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. Employee Referral Eligible As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 86,800 USD - 165,200 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
11/12/2026
Full time
Job Description Date Posted: 2026-11-03 Country: United States of America Location: US-IA-CEDAR RAPIDS- Collins Rd NE BLDG 121 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: The ability to obtain and maintain a U.S. government issued security clearance is required. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: DoD Clearance: Secret Security Clearance Status: Active and existing security clearance required after day 1 Collins Aerospace, an RTX company, is a leader in technologically advanced and intelligent solutions for the global aerospace and defense industry. Collins Aerospace has the capabilities, comprehensive portfolio, and expertise to solve customers' toughest challenges and to meet the demands of a rapidly evolving global market. Collins Aerospace is seeking a Senior Technical Project Manager - Tactical Communications to join our team in Cedar Rapids, IA. We're looking for a driven and experienced Technical Project Manager to lead a cross-functional Integrated Product Team focused on developing next-generation Tactical Communications. This role plays a key part in advancing technologies from early-stage concepts (low TRL) through accreditation and full-scale production. As part of our Systems Engineering team, you'll work on cutting-edge solutions that support critical programs. We value innovation, collaboration, and continuous learning. If you're a creative problem-solver with a passion for technology and a desire to make a real impact, we want to hear from you. Join us and help shape the future of secure systems! What You Will Do Work closely with a highly motivated and dedicated team of professionals while managing product development efforts, helping to provide solutions to our customers. Interact and interface with External Customers Participate in the systems development lifecycle, including trade study execution, system design, requirements definition, requirements allocation, requirement traceability, systems integration, verification testing and qualification. Manage Program Schedule and Cost Performance (SPI / CPI) for small to medium sized programs, and help generate the Integrated Master Schedule (IMS) Collaborate across many engineering disciplines to execute program milestones Facilitate customer demonstration events and program reviews both onsite and offsite Lead bid and proposal efforts Present technical and program data to customers and executive leadership Ability to travel up to 10% Sign-on Bonus Eligible Qualifications You Must Have Experience with the systems development process, including requirements capture, design, decomposition, integration, and verification/validation. Experience working in an Agile environment. The ability to obtain and maintain a U.S. government issued Secret security clearance is required . U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) and minimum 5 years prior relevant experience or an Advanced Degree in a related field and minimum 3 years of experience Qualifications We Prefer Experience working in an Agile environment. Proven leadership of large-scale programs throughout the full development lifecycle, with strong proficiency in project management methodologies (e.g., Waterfall, SAFe Agile) and financial tools such as Earned Value Management (EVM). Exceptionally strong communication skills, experience presenting technical data to program management and executive leadership. Prior experience in a defense or aerospace engineering environment. What We Offer • Medical, dental, and vision insurance • Three weeks of vacation for newly hired employees • Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option • Tuition reimbursement program • Student Loan Repayment Program • Life insurance and disability coverage • Optional coverages you can buy pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection • Birth, adoption, parental leave benefits • Ovia Health, fertility, and family planning • Adoption Assistance • Autism Benefit • Employee Assistance Plan, including up to 10 free counseling sessions • Healthy You Incentives, wellness rewards program • Doctor on Demand, virtual doctor visits • Bright Horizons, child and elder care services • Teladoc Medical Experts, second opinion program • This position is eligible for relocation assistance • And more! Learn More & Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customers succeed today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please ensure the role type (defined below) is appropriate for your needs before applying to this role. ONSITE: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. Employee Referral Eligible As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 86,800 USD - 165,200 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills. Hired applicants may be eligible for benefits, including but not limited to, medical, dental, vision, life insurance, short-term disability, long-term disability, 401(k) match, flexible spending accounts, flexible work schedules, employee assistance program, Employee Scholar Program, parental leave, paid time off, and holidays. Specific benefits are dependent upon the specific business unit as well as whether or not the position is covered by a collective-bargaining agreement. Hired applicants may be eligible for annual short-term and/or long-term incentive compensation programs depending on the level of the position and whether or not it is covered by a collective-bargaining agreement. Payments under these annual programs are not guaranteed and are dependent upon a variety of factors including, but not limited to, individual performance, business unit performance, and/or the company's performance. This role is a U.S.-based role. If the successful candidate resides in a U.S. territory, the appropriate pay structure and benefits will apply. RTX anticipates the application window closing approximately 40 days from the date the notice was posted. However, factors such as candidate flow and business necessity may require RTX to shorten or extend the application window. RTX is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or veteran status, or any other applicable state or federal protected class. RTX provides affirmative action in employment for qualified Individuals with a Disability and Protected Veterans in compliance with Section 503 of the Rehabilitation Act and the Vietnam Era Veterans' Readjustment Assistance Act. Privacy Policy and Terms: Click on this link to read the Policy and Terms
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.
11/12/2026
Full time
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.
11/12/2026
Full time
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 Date Posted: 2026-11-03 Country: United States of America Location: US-TX-RICHARDSON-C Cityline Dr CITYLINE C17 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: Top Secret - Current Security Clearance Status: Active and existing security clearance required on day 1 At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Collins Aerospace is a leader in technologically advanced, intelligent solutions that help redefine the aerospace and defense industry. With a comprehensive portfolio and deep technical expertise, we help customers meet the demands of the global market. Join us and help shape the future of aerospace and defense. Are you ready to make a meaningful impact while advancing your systems engineering career? At Collins Aerospace, you'll help shape the future of aerospace technology by supporting mission-critical systems that strengthen national defense and protect those who serve. As a Senior Systems Engineer within Strategic Defense Solutions (SDS), you will apply your technical expertise to the development of advanced Strategic Nuclear Deterrence capabilities, contributing to the design, integration, and validation of secure, resilient mission systems. In this role, you will collaborate across geographically distributed teams to support systems and cybersecurity engineering efforts for next-generation network, compute, and storage technologies-from early-stage development through accreditation and production. This position offers the opportunity to expand your leadership and technical capabilities while working alongside experienced professionals in communications and nuclear deterrence. We are seeking motivated engineers who thrive in solving complex problems, driving technical execution, and collaborating across disciplines to deliver innovative solutions for critical missions. This position is located onsite in Richardson, TX and requires an active Top Secret clearance. What YOU Will Do: Develop, analyze and maintain system level requirements related to network infrastructure connectivity and system protection. Evaluate network and system architectures against established technical standards to identify vulnerabilities, dependencies, and potential operational risks. Support network hardening and system integrity through the implementation of access controls, traffic filtering and secure communication mechanisms. Analyze network configuration, protocols, system access mechanisms to identify weaknesses and recommend improvements. Building complicated models using SysML for activity, state, block diagrams, and use cases. Develop and verify test procedures at the system and sub-system levels. Utilize VMWare or other cloud platforms (AWS, GCP, Azure, Oracle) in cloud and hybrid environments. Utilize JIRA to decompose features into user stories with clear acceptance criteria. Support communication between functional discipline groups within the organization. Engage in classified lab environments to apply your expertise hands-on. Utilize effective communication skills with all levels of management, program team members, and external stakeholders. What YOU Will Learn: Advanced aerospace and defense solutions supporting mission-critical military platforms. Application of Model-Based Systems Engineering (MBSE) principles to emerging technologies and complex system architectures. Hands-on experience with next-generation network, compute, storage, and cloud-based technologies. Leadership and operational excellence through Collins Aerospace's Customer Oriented Results and Excellence (CORE) Operating System. Qualifications YOU Must Have: Understanding of network architectures, TCP/IP protocols, routing, switching, and VLANs. Familiarity with network segmentation, access control mechanisms, firewalls, and infrastructure protection techniques. Proven experience in systems engineering to include capturing customer requirements, operations concept development, deriving and tracing system and sub-system requirements. Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) unless prohibited by local laws/regulations and minimum 5 years prior relevant experience or an Advanced Degree in a related field and minimum 3 years of experience. Active and transferable U.S. government DoD issued Top-Secret security clearance prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance. Qualifications We Prefer: Experience with Model-Based Systems Engineering (MBSE), including development of SysML models such as activity, state, block diagrams, and use cases using tools like MagicDraw Cameo, Rhapsody, or Enterprise Architect. Experience with Linux Operating System. Familiarity with National Institute of Standards and Technology (NIST) standards, Risk Management Framework (RMF) processes, and Defense Information Systems Agency Security Technical Implementation Guides (DISA STIGs). Agile experience working as a scrum master, Product Owner (PO), Release Train Engineer (RTE), or other agile leadership roles. Experience with processes such as peer review, configuration, and change management. Participating in root cause analysis and corrective actions. Identify, assess, and manage technical risks and opportunities to support program execution, system performance, and successful delivery of mission objectives. What we offer: Some of our competitive benefits packages include: Medical, dental, and vision insurance Three weeks of vacation for newly hired employees Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option Tuition reimbursement program Student Loan Repayment Program Life insurance and disability coverage Optional coverage: you can purchase pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection Birth, adoption, parental leave benefits Ovia Health, fertility, and family planning Adoption Assistance Autism Benefit Employee Assistance Plan, including up to 10 free counseling sessions Healthy You Incentives, wellness rewards program Doctor on Demand, Virtual doctor visits Bright Horizons, child and elder care services Teladoc Medical Experts, second opinion program Relocation assistance may be provided And more! Learn More and Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customers succeed today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please consider the following role type definitions as you apply for this role. Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other - propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 95,500 USD - 181,700 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills . click apply for full job details
11/12/2026
Full time
Job Description Date Posted: 2026-11-03 Country: United States of America Location: US-TX-RICHARDSON-C Cityline Dr CITYLINE C17 Position Role Type: Onsite U.S. Citizen, U.S. Person, or Immigration Status Requirements: Active and transferable U.S. government issued security clearance is required prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance Security Clearance Type: Top Secret - Current Security Clearance Status: Active and existing security clearance required on day 1 At RTX, the world's largest aerospace and defense company, 185,000 great minds are united by purpose and inspired to make a difference solving the world's most complex problems. With our three market leading businesses, world-class operations and investments in research and development, we offer capabilities and opportunity no one else can. Together, we push the boundaries of known science and find new ways to connect and protect our world. Collins Aerospace is a leader in technologically advanced, intelligent solutions that help redefine the aerospace and defense industry. With a comprehensive portfolio and deep technical expertise, we help customers meet the demands of the global market. Join us and help shape the future of aerospace and defense. Are you ready to make a meaningful impact while advancing your systems engineering career? At Collins Aerospace, you'll help shape the future of aerospace technology by supporting mission-critical systems that strengthen national defense and protect those who serve. As a Senior Systems Engineer within Strategic Defense Solutions (SDS), you will apply your technical expertise to the development of advanced Strategic Nuclear Deterrence capabilities, contributing to the design, integration, and validation of secure, resilient mission systems. In this role, you will collaborate across geographically distributed teams to support systems and cybersecurity engineering efforts for next-generation network, compute, and storage technologies-from early-stage development through accreditation and production. This position offers the opportunity to expand your leadership and technical capabilities while working alongside experienced professionals in communications and nuclear deterrence. We are seeking motivated engineers who thrive in solving complex problems, driving technical execution, and collaborating across disciplines to deliver innovative solutions for critical missions. This position is located onsite in Richardson, TX and requires an active Top Secret clearance. What YOU Will Do: Develop, analyze and maintain system level requirements related to network infrastructure connectivity and system protection. Evaluate network and system architectures against established technical standards to identify vulnerabilities, dependencies, and potential operational risks. Support network hardening and system integrity through the implementation of access controls, traffic filtering and secure communication mechanisms. Analyze network configuration, protocols, system access mechanisms to identify weaknesses and recommend improvements. Building complicated models using SysML for activity, state, block diagrams, and use cases. Develop and verify test procedures at the system and sub-system levels. Utilize VMWare or other cloud platforms (AWS, GCP, Azure, Oracle) in cloud and hybrid environments. Utilize JIRA to decompose features into user stories with clear acceptance criteria. Support communication between functional discipline groups within the organization. Engage in classified lab environments to apply your expertise hands-on. Utilize effective communication skills with all levels of management, program team members, and external stakeholders. What YOU Will Learn: Advanced aerospace and defense solutions supporting mission-critical military platforms. Application of Model-Based Systems Engineering (MBSE) principles to emerging technologies and complex system architectures. Hands-on experience with next-generation network, compute, storage, and cloud-based technologies. Leadership and operational excellence through Collins Aerospace's Customer Oriented Results and Excellence (CORE) Operating System. Qualifications YOU Must Have: Understanding of network architectures, TCP/IP protocols, routing, switching, and VLANs. Familiarity with network segmentation, access control mechanisms, firewalls, and infrastructure protection techniques. Proven experience in systems engineering to include capturing customer requirements, operations concept development, deriving and tracing system and sub-system requirements. Typically requires a degree in Science, Technology, Engineering or Mathematics (STEM) unless prohibited by local laws/regulations and minimum 5 years prior relevant experience or an Advanced Degree in a related field and minimum 3 years of experience. Active and transferable U.S. government DoD issued Top-Secret security clearance prior to start date. U.S. citizenship is required, as only U.S. citizens are eligible for a security clearance. Qualifications We Prefer: Experience with Model-Based Systems Engineering (MBSE), including development of SysML models such as activity, state, block diagrams, and use cases using tools like MagicDraw Cameo, Rhapsody, or Enterprise Architect. Experience with Linux Operating System. Familiarity with National Institute of Standards and Technology (NIST) standards, Risk Management Framework (RMF) processes, and Defense Information Systems Agency Security Technical Implementation Guides (DISA STIGs). Agile experience working as a scrum master, Product Owner (PO), Release Train Engineer (RTE), or other agile leadership roles. Experience with processes such as peer review, configuration, and change management. Participating in root cause analysis and corrective actions. Identify, assess, and manage technical risks and opportunities to support program execution, system performance, and successful delivery of mission objectives. What we offer: Some of our competitive benefits packages include: Medical, dental, and vision insurance Three weeks of vacation for newly hired employees Generous 401(k) plan that includes employer matching funds and separate employer retirement contribution, including a Lifetime Income Strategy option Tuition reimbursement program Student Loan Repayment Program Life insurance and disability coverage Optional coverage: you can purchase pet insurance, home and auto insurance, additional life and accident insurance, critical illness insurance, group legal, ID theft protection Birth, adoption, parental leave benefits Ovia Health, fertility, and family planning Adoption Assistance Autism Benefit Employee Assistance Plan, including up to 10 free counseling sessions Healthy You Incentives, wellness rewards program Doctor on Demand, Virtual doctor visits Bright Horizons, child and elder care services Teladoc Medical Experts, second opinion program Relocation assistance may be provided And more! Learn More and Apply Now! Do you want to be a part of something bigger? A team whose impact stretches across the world, and even beyond? At Collins Aerospace, our Mission Systems team helps civilian, military and government customers complete their most complex missions - whatever and wherever they may be. Our customers depend on us for intelligent and secure communications, missionized systems for specialized aircraft and spacecraft and collaborative space solutions. By joining our team, you'll have your own critical part to play in ensuring our customers succeed today while anticipating their needs for tomorrow. Are you up for the challenge? Join our mission today. Please consider the following role type definitions as you apply for this role. Onsite: Employees who are working in Onsite roles will work primarily onsite. This includes all production and maintenance employees, as they are essential to the development of our products. At Collins, the paths we pave together lead to limitless possibility. And the bonds we form - with our customers and with each other - propel us all higher, again and again. Apply now and be part of the team that's redefining aerospace, every day. As part of our commitment to maintaining a secure hiring process, candidates may be asked to attend select steps of the interview process in-person at one of our office locations, regardless of whether the role is designated as on-site, hybrid or remote. The salary range for this role is 95,500 USD - 181,700 USD. The salary range provided is a good faith estimate representative of all experience levels. RTX considers several factors when extending an offer, including but not limited to, the role, function and associated responsibilities, a candidate's work experience, location, education/training, and key skills . click apply for full job details
