Life Sciences AI Assessment Expert (Remote)

Aitrainer; San Francisco (CA); Remote; USD 90,000 - 120,000; Full time

Benefits offered by this job

Flexible hours

Remote work environment

Competitive compensation

Job summary

Turing in San Francisco is seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This non-coding role focuses on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning.

You will create challenging problems based on realistic research scenarios, experimental results, data, figures, sequences, structures, and literature-style evidence.

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 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

Graduate-level life sciences

Scientific reasoning

Data interpretation

Education

Ph.D., postdoc or equivalent

Tools

None

Job description

Turing in San Francisco is seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This non-coding role focuses on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning.

You will create challenging problems based on realistic research scenarios, experimental results, data, figures, sequences, structures, and literature-style evidence.