Life Sciences Chemistry Subject Matter Expert Task Designer

The partner title and supplied description name different subjects. Review the duties and qualifications before applying.

You will design challenging life sciences problems that test whether AI systems can interpret evidence, analyze data, and reason through realistic research scenarios. This is a non-coding role focused on creating precise questions and defensible reference answers.

The partner title identifies chemistry subject-matter expertise. Review the duties and qualifications before applying, since the work description focuses on broader life sciences scientific reasoning and task design.

• Create graduate-level problems based on realistic research contexts.
• Write clear questions with rigorous reference answers.
• Interpret experiments, datasets, figures, sequences, structures, and literature-style biological evidence.
• Check biological claims, calculations, assumptions, and conclusions.
• Identify ambiguity, alternative interpretations, and questions that lack enough constraints.
• Keep tasks self-contained, reproducible where applicable, and clearly documented.
• Use reviewer feedback while maintaining quality and throughput.

The role is a remote contractor assignment lasting one month. It is listed as entry level, while the work requires advanced scientific research skills and the ability to produce graduate-level material.

• Pay: No rate is provided for this role.
• Schedule: 20+ hours per week.
• Engagement: Contractor and part-time assignment lasting one month.
• Time overlap: At least four hours of overlap with Pacific Time.
• Location: Remote and open worldwide.
• Language: Fluent English.
• Education and experience: Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences.
• Subject expertise: Chemistry or another biological discipline such as molecular biology, genetics and genomics, cell biology, biochemistry, neuroscience, microbiology, immunology, ecology, structural biology, or pharmacology.
• Required skills: Interpret scientific experiments, data, and research findings; write precisely; explain assumptions; assess competing interpretations; and create self-contained scientific questions.
• Helpful background: Designing graduate-level problems or assessments, reviewing scientific manuscripts, evaluating AI-generated scientific responses, or working with AI and machine learning evaluation datasets.
• Also valued: Peer-reviewed publications or graduate-level teaching and mentoring experience.

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

AI training is the human work behind systems that learn from examples, including writing and reviewing questions, answers, and scientific reasoning tasks. People with specialist knowledge help evaluate whether AI outputs are accurate, well supported, and useful.

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