Computational Life Sciences Designer, Computer Science STEM

The work

You will design challenging workflows that test whether AI agents can complete authentic computational life sciences work. Each task should involve several steps, such as inspecting data, choosing methods, running code, troubleshooting problems, analyzing results, and producing scientifically valid outputs.

You will prepare the materials needed to run and assess each task, then verify that the science, calculations, code, and intermediate results are correct. Tasks must be self-contained and executable in a network-isolated sandbox.

• Create realistic datasets, input files, instructions, constraints, and expected deliverables.
• Write reproducible expert solutions and objectively verifiable ground truths.
• Develop automated or partly automated grading criteria.
• Validate scientific assumptions, calculations, code, and computational outputs.
• Design workflows across computational life sciences, including areas such as bioinformatics, genomics, systems biology, neuroscience, biostatistics, drug discovery, biochemistry, structural biology, protein engineering, and microbiology.

What it pays and takes

This is a remote contractor assignment lasting five weeks. The work requires strong research and programming experience, especially in Python.

• $280 per approved task.
• At least 40 hours per week.
• A four-hour work overlap with Pacific Time.
• A Ph.D., postdoctoral training, or equivalent research experience in the life sciences.
• Strong scientific programming experience, particularly in Python.
• Experience with multi-step computational analyses, scientific libraries, command-line tools, and reproducible research pipelines.
• Ability to independently validate scientific reasoning and computational results.
• This assignment does not include medical leave or paid leave.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work uses examples, evaluations, and human feedback to help artificial intelligence systems perform useful tasks. People with research and technical expertise are needed to create reliable tests and judge whether an AI system's work is scientifically sound.