AI Trainer (Life Sciences)

What You'll Do7

• Design realistic computational life-science problems that test the limits of advanced AI models.
• Write clear task instructions, set up reproducible environments, and provide reference solutions and evaluation criteria.
• Review model-produced code, analyses, models, experiments, and technical deliverables for accuracy.
• Spot invalid biological assumptions, methodological errors, data leakage, statistical flaws, or reproducibility failures.
• Establish objective standards for what counts as a correct solution.
• Work with Python and scientific libraries to create and validate tasks.
• Document evaluation criteria and maintain consistency across tasks.

Requirements5

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Strong practical proficiency with Python.
• Deep expertise in at least one life-science specialization such as biology, computational biology, bioinformatics, medicine and health, biomedical science, neuroscience, genomics, ecology, or evolution.
• Hands-on experience with computational methods for modeling, simulation, bioinformatics, biomedical analysis, statistics, or reproducible research.
• Ability to provide public proof of skill, such as peer-reviewed publications or preprints with computational methods, public research code or bioinformatics pipelines, Google Scholar or ORCID profiles, PhysioNet or other biomedical competition results, open-source scientific software contributions, or public datasets, models, notebooks, or technical projects.

Preferred Qualifications5

• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience reviewing other AI trainers’ technical submissions.
• Familiarity with NumPy, SciPy, pandas, Biopython, PyTorch, JAX, or similar scientific tools.
• Prior work with long-horizon or agentic AI evaluations.
• Availability to begin immediately.

Who Should Apply

Computational life scientists with paid AI-training or model-evaluation experience and strong Python skills. You should have deep expertise in a life-science field and a track record of computational work. Public evidence of your skills, such as publications or code, is expected.

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