About the role

DataAnnotation is looking for an experienced Biomedical Researcher - AI Trainer 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 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. 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.