The work
You will create advanced, non-code life sciences tasks that test whether AI systems can reason from realistic research evidence. Your work may use experiments, biological data, figures, sequences, structures, or literature-style findings.
The role title identifies this as Software Engineer - Code, while the duties focus on life sciences reasoning evaluation. Review the duties and qualifications carefully before applying.
- Design novel, graduate-level problems based on realistic research scenarios.
- Write clear questions and rigorous reference answers with defensible scientific reasoning.
- Interpret experiments, datasets, figures, sequences, structures, and other biological evidence.
- Check biological claims, calculations, assumptions, and conclusions.
- Find ambiguity, alternative interpretations, and questions that are not sufficiently constrained.
- Make tasks self-contained, reproducible where appropriate, and clearly documented.
- Use reviewer feedback while maintaining quality and throughput.
What it pays and takes
The listing does not provide a pay rate. This is a part-time contractor role open worldwide, with a commitment of at least 20 hours per week.
- Pay: Not provided in the listing.
- Schedule: 20+ hours per week.
- Work arrangement: Part-time contract work.
- Location: Open worldwide.
- Language: Professional fluency in English.
- Required background: Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences.
- Required skills: Strong expertise in at least one biological discipline, scientific experiment and data interpretation, and precise scientific writing.
- You must be able to create questions that reward synthesis and scientific reasoning rather than simple recall.
Helpful background
Experience creating graduate-level problems, assessments, or research exercises can help. Scientific manuscript review, AI-generated response evaluation, or work with AI and machine learning evaluation datasets is also relevant.
Peer-reviewed publications or graduate-level teaching and mentoring may support success. Relevant fields include molecular biology, genetics and genomics, cell biology, biochemistry, neuroscience, microbiology, immunology, evolutionary biology, ecology, structural biology, biophysics, developmental biology, cancer biology, pharmacology, and related life sciences.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training work is the human work behind systems that learn from examples, including writing tasks and reviewing model answers. OpenTrain helps people find these projects, build a profile, and apply in one place; specialized expertise matters because it supports accurate, useful evaluations.