Life Sciences Scientific Reasoning Task Designer

You will design advanced scientific reasoning tasks for AI training and evaluation. The work focuses on realistic biological research scenarios and tests synthesis and interpretation rather than simple factual recall.

Each task must include an objective question and a scientifically defensible reference answer. You will also check the evidence, reasoning, calculations, assumptions, and conclusions behind each task.

• Create novel, graduate-level problems grounded in realistic life sciences research.
• Interpret experiments, datasets, figures, sequences, structures, and literature-style evidence.
• Write clear, self-contained questions and rigorous reference answers.
• Check biological claims, calculations, assumptions, and conclusions for accuracy.
• Identify ambiguity, alternative interpretations, and questions that lack enough constraints.
• Document tasks clearly, maintain reproducibility where appropriate, and use reviewer feedback to maintain quality and throughput.
• This is scientific reasoning and task creation work, not programming or code execution.

What It Pays and Takes

The listing does not provide a pay rate. The role is a remote contractor assignment for one month.

• Pay: Not specified in the listing.
• Schedule: The role description requires at least 40 hours per week, including 4 hours of overlap with Pacific Time. The structured listing also marks the role as 20+ hours per week.
• Location: Remote and open worldwide.
• Language: English.
• Experience: The listing is marked entry level, but the role requires a Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences.
• Expertise: Strong knowledge in at least one biological discipline and the ability to interpret scientific experiments, data, and research findings.
• Skills: Strong scientific writing, careful attention to precision, complex evidence-based reasoning, and clear communication of conclusions.

Helpful Background

Experience creating graduate-level problems, assessments, or research exercises is useful. Relevant experience may also include reviewing scientific manuscripts, explaining complex scientific ideas through precise questions, evaluating AI-generated scientific responses, working with AI or machine-learning evaluation datasets, publishing peer-reviewed research, or teaching and mentoring graduate students.

How It Works

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

About AI Training Work

AI training is the human work behind systems that learn from examples, including writing and reviewing model responses, evaluating evidence, and preparing specialist data. People with deep subject knowledge help make those examples accurate, clear, and useful.