Remote Life Sciences Computational Workflow Expert

OpenTrain AI, Inc.; United States; Remote; Full time

Job summary

OpenTrain AI, Inc. is seeking a remote contractor to design realistic computational life sciences workflows used to evaluate AI agents.

Each task should require data inspection, method selection, code execution, troubleshooting, analysis, and synthesis. You will prepare the files, datasets, instructions, constraints, expected deliverables, expert solutions, ground truths, and grading criteria needed for each task.

Qualifications

• Ph.D. in Life Sciences or equivalent research experience.
• Strong Python programming skills.
• Experience designing multi-step computational analyses.
• Professional fluency in English.

Responsibilities

• Design realistic computational life sciences workflows used to evaluate AI agents.
• Prepare files, datasets, instructions, constraints, deliverables, expert solutions, ground truths, and grading criteria.
• Confirm scientific assumptions, calculations, code, intermediate results, and final answers are correct and reproducible in network-isolated environments.
• Build multi-step workflows in bioinformatics, computational genomics, systems biology, biostatistics, computational drug discovery, and related fields.
• Create self-contained, executable tasks suitable for objective evaluation of AI-generated work.
• Validate scientific reasoning, computational outputs, code, calculations, and final conclusions.

Skills

Python

Life sciences background

Computational skills

English proficiency

Education

Ph.D. in Life Sciences

Postdoctoral experience

Job description

OpenTrain AI, Inc. is seeking a remote contractor to design realistic computational life sciences workflows used to evaluate AI agents.

Each task should require data inspection, method selection, code execution, troubleshooting, analysis, and synthesis. You will prepare the files, datasets, instructions, constraints, expected deliverables, expert solutions, ground truths, and grading criteria needed for each task.