Agentic Life Science Expert - Remote Computational Biology
Listing checked October 1, 2026 · pay as published by Turing
Overview
Turing needs life sciences researchers who can turn authentic scientific workflows into structured tasks that AI agents execute from start to finish. You will build challenges where an agent inspects data, writes and runs code, picks the right computational method, troubleshoots midway failures, and lands on a defensible scientific result. Tasks live in controlled, network-isolated sandboxes, so everything from Python scripts to command-line tools and domain software must be self-contained and reproducible. Your work directly shapes how well AI systems handle real multi-step science rather than simple question answering. Expect to validate assumptions, code, and outputs, then package each task with verifiable ground truth and grading criteria.
What You'll Do8
- 1Design realistic, multi-step agentic workflows spanning life sciences subfields like bioinformatics, computational genomics, and structural biology.
- 2Build tasks that force an agent through data inspection, method selection, execution, and result synthesis instead of single-script answers.
- 3Create realistic input files, datasets, instructions, constraints, and deliverable specifications for each task.
- 4Write reproducible expert solutions and objective ground truths that run inside the controlled sandbox.
- 5Develop automated or semi-automated grading criteria that measure scientific reasoning, not memorization.
- 6Validate scientific assumptions, calculations, code, intermediate outputs, and final answers before submission.
- 7Ensure every task stays self-contained and executable in network-isolated computational environments.
- 8Respond to reviewer feedback and sustain quality and throughput across a task pipeline.
Requirements8
- 1Ph.D., postdoctoral, or equivalent research background in the life sciences.
- 2Proven scientific programming experience, with Python as the primary language.
- 3Hands-on record of running multi-step computational scientific analyses end to end.
- 4Ability to independently check both the scientific reasoning and the computational output of an analysis.
- 5Expertise in at least one domain such as bioinformatics, computational genomics, systems biology, computational neuroscience, biostatistics, computational drug discovery, computational biochemistry, structural biology, protein engineering, or computational microbiology.
- 6Experience with scientific libraries and command-line tooling.
- 7Track record building reproducible research pipelines.
- 8Skill at translating open-ended research workflows into bounded, gradable tasks.
Who Should Apply
Researchers with a life sciences Ph.D. or postdoc who code in Python daily and have taken analyses through many computational steps fit this role well. Ideal candidates have deep domain knowledge in one specialty plus the discipline to make pipelines reproducible and outputs verifiable. Experience with Docker, Linux, coding agents, or automated evaluation environments gives you an extra edge, as does a publication record in data-intensive work. The role suits people who enjoy writing scientific code and grading rigor; it is a poor match for those who mostly ran wet-lab experiments or only wrote single-script analyses. Candidates miss out when their sample tasks stay at the question-and-answer level instead of demanding agent decisions, and when they submit solutions that cannot run offline or lack a clear ground truth.
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Required Skills
Application Tip
Show one concrete multi-step Python analysis in your application, naming the libraries, the intermediate checkpoints, and how you would grade an agent's output at each step. Mention any Docker or Linux environment work and your life sciences specialty, since reviewers screen for reproducible, sandbox-ready tasks with verifiable ground truth.
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