Agentic Life Science Expert

This role focuses on designing computational life sciences workflows that show whether AI agents can complete authentic, multi-step scientific work. The listing title is Agentic Life Science Expert, while the duties focus on computational life sciences task design, so review the duties and qualifications before applying.

You will create self-contained tasks with scientific data, executable code, reproducible solutions, verifiable answers, and grading rules. Your work will test scientific reasoning and execution, not simple recall.

• Design realistic agentic workflows across computational life sciences.
• Create input files, datasets, instructions, constraints, and expected deliverables for multi-step analyses.
• Build tasks involving data inspection, method selection, code execution, troubleshooting, analysis, and synthesis.
• Check scientific assumptions, calculations, code, intermediate outputs, and final answers.
• Create automated or partly automated grading criteria for scientific reasoning and execution.
• Ensure tasks run reproducibly in controlled, network-isolated sandbox environments.
• Use reviewer feedback while maintaining quality and throughput.

This is a remote contractor assignment for a five-week contract. The work requires at least 20 hours per week and includes a four-hour overlap with Pacific Time.

• Pay: $280 per approved task.
• Schedule: At least 20 hours per week for five weeks.
• Time-zone requirement: Four hours of overlap with Pacific Time.
• Location: Remote and open worldwide.
• Education: Ph.D., postdoctoral experience, or equivalent research experience in the life sciences.
• Programming: Strong scientific programming experience, particularly in Python.
• Analysis: Experience with multi-step computational scientific analyses and independently validating scientific reasoning and computational outputs.
• Task design: Ability to create bounded, objectively gradable tasks from authentic research workflows.
• Useful background: Bioinformatics, computational genomics, systems biology, computational neuroscience, biostatistics, computational drug discovery, computational biochemistry, structural biology, protein engineering, or computational microbiology.
• Additional experience: Scientific libraries, command-line tools, and reproducible research pipelines.

Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is where you find the role and start the application; it does not employ or pay contributors for this assignment.

AI training is the human work behind systems that learn from examples, including writing, reviewing, coding, and evaluating model outputs. People with strong scientific experience help make these systems more accurate by testing whether their reasoning and results hold up on real research tasks.