The work
You will create difficult, realistic tasks that test advanced AI systems on computational life-science problems. You will also assess whether model-generated technical work is correct, scientifically sound, reproducible, and free of serious errors.
- Create computational life-science tasks, clear instructions, reproducible environments, reference solutions, and objective evaluation criteria.
- Review model-generated code, analyses, models, experiments, and other technical deliverables.
- Find invalid biological assumptions, methodological errors, data leakage, statistical problems, and reproducibility failures.
- Set standards for deciding whether technical solutions are correct and scientifically sound.
What it pays and takes
This is a remote, part-time contractor role for an intermediate-level contributor. The work requires prior experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
- Pay: $60 to $120 per hour equivalent, paid per task that meets project specifications and quality requirements.
- Schedule: At least 15 hours per week and less than 20 hours per week.
- Location: Open to candidates in the United Arab Emirates, Argentina, Austria, Bangladesh, Belgium, Bahrain, Brazil, Canada, Switzerland, Chile, Colombia, Germany, Denmark, Algeria, Egypt, Spain, France, the United Kingdom, Ghana, Greece, Indonesia, India, Italy, Jordan, Japan, Kenya, Kyrgyzstan, Kuwait, Kazakhstan, Lebanon, Sri Lanka, Morocco, Mexico, Malaysia, Nigeria, the Netherlands, Oman, Peru, the Philippines, Pakistan, Palestine, Portugal, Qatar, Saudi Arabia, Singapore, Thailand, Tunisia, Taiwan, the United States, Uzbekistan, Vietnam, and South Africa.
- Language: Fluent in English.
- Core skills: Strong practical Python proficiency and deep expertise in at least one relevant area, such as biology, computational biology, bioinformatics, medicine and health, biomedical science, neuroscience, genomics, ecology, or evolution.
- Experience: Hands-on work with computational modeling, simulation, bioinformatics, biomedical analysis, statistics, or reproducible research.
- AI training background: Paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
Helpful background
Experience building AI-training tasks, reference answers, rubrics, graders, or benchmark problems is useful. Experience reviewing technical submissions or conducting long-horizon or agentic AI evaluations is also relevant.
- Scientific tools such as NumPy, SciPy, pandas, Biopython, PyTorch, JAX, or comparable software.
- Public computational publications, research code, bioinformatics pipelines, open-source scientific software, datasets, models, notebooks, or technical projects.
- Experience applying programming judgment to scientific analyses and checking whether results can be reproduced.
How it works
You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.
The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 8 screening questions.
About AI training work
AI training is the human work behind systems that learn from examples, including reviewing code, scientific analyses, and model outputs. People with strong technical or scientific experience help set reliable standards for these systems and are paid for specialized evaluation work.