Remote Life Sciences Expert — Advanced Problem Designer

Aitrainer; San Francisco (CA); Remote; USD 83,000 - 138,000; Part time

Benefits offered by this job

Flexible working hours

Remote collaboration

Competitive compensation

Job summary

Turing is seeking Life Sciences Subject Matter Experts to craft advanced scientific reasoning tasks for LifeSciBench. This non-code benchmark emphasizes deep biology knowledge, experimental interpretation, and rigorous reasoning.

You will create challenging questions based on realistic scientific scenarios, experimental results, biological data, figures, literature-style evidence, and research problems. This is a non-code benchmark: tasks are not executed, and no programming is required.

Qualifications

• Ph.D., postdoctoral experience, or equivalent advanced research experience in the life sciences.
• Strong expertise in at least one biological discipline.
• Demonstrated ability to interpret scientific experiments, data, and research findings.
• Strong scientific writing skills and attention to precision.

Responsibilities

• Design novel, graduate-level life sciences problems grounded in realistic research contexts.
• Develop clear questions and rigorous reference answers with defensible scientific reasoning.
• Interpret experiments, datasets, figures, sequences, structures, and other biological evidence.
• Validate biological claims, calculations, assumptions, and conclusions.
• Identify ambiguity, alternative interpretations, and insufficiently constrained questions.
• Ensure tasks are self-contained, reproducible where applicable, and clearly documented.
• Incorporate reviewer feedback while maintaining quality and throughput.

Skills

Chemistry

Education

Ph.D./Postdoctoral experience

Job description

Turing is seeking Life Sciences Subject Matter Experts to craft advanced scientific reasoning tasks for LifeSciBench. This non-code benchmark emphasizes deep biology knowledge, experimental interpretation, and rigorous reasoning.

You will create challenging questions based on realistic scientific scenarios, experimental results, biological data, figures, literature-style evidence, and research problems. This is a non-code benchmark: tasks are not executed, and no programming is required.