Agentic Life Science Expert
Listing checked October 1, 2026 · pay as published by Turing
Overview
Turing seeks life sciences researchers with serious computational skills to build realistic tasks for AI agents. You will design workflows where an agent must navigate data, run code, use computational tools, fix intermediate problems, and deliver scientifically sound results. The goal is to test whether AI can carry out real multi-step scientific work, not just answer questions. Tasks run inside controlled, network-isolated sandboxes and need to be self-contained and reproducible, often using Python, command-line tools, scientific libraries, or domain software. Your work shapes how agentic systems are evaluated across fields like bioinformatics, computational genomics, and biostatistics.
What You'll Do10
- 1Design difficult, realistic agentic workflows that span multiple life science domains.
- 2Build tasks that require several computational steps instead of a single script or a question-answer response.
- 3Produce realistic input files, scientific datasets, instructions, constraints, and expected deliverables.
- 4Write tasks where agents must inspect data, choose methods, run analyses, fix issues, and combine results.
- 5Create reproducible expert solutions and objectively verifiable ground truths that execute in the sandbox.
- 6Define automated or semi-automated grading criteria that hold up under review.
- 7Make sure each task measures scientific reasoning and execution rather than memorization.
- 8Check scientific assumptions, calculations, code, intermediate outputs, and final answers.
- 9Keep every task self-contained and runnable in controlled, network-isolated computational environments.
- 10Sustain high quality and throughput while acting on reviewer feedback.
Requirements7
- 1Ph.D., postdoctoral, or equivalent research experience in the life sciences.
- 2Demonstrated computational and scientific-programming ability, especially in Python.
- 3Hands-on experience performing multi-step computational scientific analyses.
- 4Proven capacity to validate scientific reasoning and computational outputs on your own.
- 5Mastery of multi-step computational scientific analysis.
- 6Experience with scientific libraries and command-line tools.
- 7Track record creating reproducible research pipelines.
Who Should Apply
This role fits researchers who hold a Ph.D. or postdoctoral background in the life sciences and have spent real time writing Python and running multi-step computational analyses. You should be comfortable validating both the science and the code, and able to turn genuine research workflows into bounded tasks with clear, gradable outputs. Candidates with expertise in areas like bioinformatics, computational genomics, structural biology, or computational drug discovery will find the work especially relevant, as will anyone who has built reproducible research pipelines. The role is less suitable for people whose experience is mainly wet-lab or who rely on single-script analyses. Applicants often get rejected because they cannot show multi-step computational work or because their tasks lack objectively verifiable ground truths. Failing to account for network-isolated sandbox limits is another common reason fit scores drop.
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Required Skills
Application Tip
Lead with concrete evidence of multi-step computational life science work: name the Python libraries and command-line tools you used, describe the workflow steps, and point to a publication or pipeline where you built a reproducible analysis with verified outputs.
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