You will design advanced life sciences problems based on realistic research settings. The work focuses on biological reasoning and scientific communication, not programming or running code.
You will create questions and reference answers, interpret biological evidence, check scientific claims and calculations, and improve tasks based on reviewer feedback.
- Design novel, graduate-level biology problems grounded in realistic research contexts.
- Write clear questions and rigorous reference answers supported by defensible scientific reasoning.
- Interpret experiments, datasets, figures, sequences, structures, and literature-style evidence.
- Check biological claims, calculations, assumptions, and conclusions for accuracy.
- Identify ambiguity, alternative interpretations, and questions that lack enough information.
- Keep tasks self-contained, reproducible when appropriate, and clearly documented.
- Incorporate reviewer feedback while maintaining quality and throughput.
What it pays and takes
The role is a remote contractor assignment for a one-month engagement. No pay rate is provided in the listing.
You should have advanced life sciences research experience and strong expertise in at least one biological discipline. Experience creating assessments, reviewing research, or evaluating scientific AI responses can also help.
- Commitment: At least 20 hours per week.
- Location: Remote and open worldwide.
- Language: Professional fluency in English.
- Background: Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences.
- Skills: Interpret experiments, data, and research findings with precision.
- Skills: Write clearly and turn complex scientific ideas into precise, self-contained questions.
- Helpful experience: Graduate-level problem design, scientific manuscript review, peer-reviewed publications, teaching, mentoring, or AI and machine learning evaluation datasets.
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 human-written examples, reviews, and evaluations to help improve how AI systems understand and answer questions. People with strong scientific knowledge are needed to judge evidence, catch errors, and create reliable examples for specialized subjects.