Life Sciences AI Trainer

When AI gets the details wrong in your field, real people pay for it. The people who know the work best have to be the ones training these models. You’re not being replaced by AI. You’re making sure it’s used accurately and safely.

• Design challenging, realistic computational-biology tasks based on real-world workflows, including scenarios, colleague-style prompts, and supporting files such as count matrices, metadata, VCFs, QC outputs, annotations, notebooks, pipeline logs, and correspondence.
• Run tasks through frontier AI agents and evaluate the resulting analysis reports, annotated tables, figures, and notebooks against professional scientific standards.
• Compare multiple AI outputs using the same prompts and files, determine which performs better, and document where each response succeeds or falls short.
• Create detailed grading rubrics defining the requirements for a correct analysis, including appropriate normalization, statistical methods, multiple-testing corrections, and biological interpretation.
• Identify and document concrete scientific and technical failures, including ignored batch effects or confounders, inappropriate statistical tests, uncorrected p-values, incorrect annotation versions, dropped samples, fabricated results, misleading code, and off-brief interpretations.
• Contribute expertise across genomics and variant interpretation, bulk and single-cell transcriptomics, proteomics and multiomics, population and statistical genetics, and machine learning for biology.
• Review and refine tasks developed by other domain experts.
• Draw on personal professional experience to create scenarios that test whether AI systems can perform meaningful life-sciences analysis work.

• At least 2 years of hands-on experience analyzing real biological data in industry, an academic lab, or a research institute, excluding undergraduate education.
• Bachelor's degree in progress or higher in Biology or a directly related field, completed in the United States, Canada, Europe, or the United Kingdom; related fields may include molecular or cell biology, genetics, immunology, neuroscience, biochemistry, bioinformatics, computational biology, biostatistics, computer science, or statistics when paired with substantial biological-data experience.
• Ability to write, debug, and explain analysis code in Python and/or R; proficiency in both is preferred, and proficiency in at least one is required.
• Deep experience in at least one area: genomics and variant interpretation; bulk or single-cell transcriptomics; proteomics, metabolomics, or multiomics integration; population or statistical genetics; machine learning applied to biology; clinical genomics; metagenomics; or phylogenetics.
• Working understanding of multiple adjacent life-sciences analysis areas and the ability to assess workflows outside a primary specialization.
• Experience independently owning a multistep analysis from raw or messy data through cleaning, processing, and final interpretation.
• Familiarity with reproducible research practices, including notebooks, scripts, version control, and workflow tools.
• Strong scientific judgment regarding normalization, batch effects, confounding, multiple-testing correction, statistical power, and biological interpretation.
• Current or recent hands-on individual-contributor experience performing biological-data analysis rather than exclusively managing others.
• Full professional or native-level written and spoken English, with the ability to clearly explain complex scientific reasoning and why an analysis is incorrect.
• Strong attention to detail, comfort with ambiguity, and the ability to verify document claims against underlying data.
• Ability to interpret feedback critically, work independently, and…