Agentic Life Science Expert

Careerflow.ai•, CO

2d•Remote

About The Position

Careerflow Human Data Labs partners with AI companies to bring real-world professional expertise into their products. We are seeking Computational Life Sciences experts with strong scientific programming skills to develop complex, realistic tasks for Agentic Life Sciences. You will design tasks where AI agents must independently navigate scientific data, write and execute code, use computational tools, troubleshoot intermediate results, and produce scientifically valid final outputs. The goal is to evaluate whether AI systems can perform authentic multi-step scientific work — not simply answer scientific questions. Tasks are executed in controlled computational environments and should be self-contained and reproducible. Depending on the scientific workflow, tasks may use Python, command-line tools, scientific libraries, or domain-specific software. Experts should design tasks whose required dependencies, data, and computational resources can be reliably packaged and evaluated in the project environment.

Requirements

Ph.D., postdoctoral, or equivalent research experience in the life sciences, with strong demonstrated computational and scientific-programming experience.
Strong scientific programming experience, particularly in Python.
Experience performing multi-step computational scientific analyses.
Ability to independently validate both scientific reasoning and computational outputs.

Nice To Haves

Expertise in one or more areas including bioinformatics, computational genomics, systems biology, computational neuroscience, biostatistics, computational drug discovery, computational biochemistry, structural biology, protein engineering, or computational microbiology.
Experience with scientific libraries and command-line tools.
Experience creating reproducible research pipelines.
Ability to translate authentic research workflows into bounded, objectively gradable tasks.
Experience with AI agents, coding agents, or scientific AI systems.
Experience building automated evaluation environments.
Familiarity with Docker/Linux environments.
Publications involving computational or data-intensive life sciences research.

Responsibilities

Design challenging, realistic agentic scientific workflows across the life sciences.
Create tasks requiring multiple computational steps rather than single-script or simple question-answer solutions.
Develop realistic input files, scientific datasets, instructions, constraints, and expected deliverables.
Build tasks requiring agents to inspect data, select appropriate methods, execute analyses, troubleshoot problems, and synthesize results.
Create reproducible expert solutions and objectively verifiable ground truths that run in the sandbox described above.
Design robust automated or semi-automated grading criteria.
Ensure tasks measure scientific reasoning and execution rather than memorization.
Validate scientific assumptions, calculations, code, intermediate outputs, and final answers.
Ensure tasks are self-contained and executable in controlled, network-isolated computational environments.
Maintain high quality and throughput while responding effectively to reviewer feedback.