Design graduate-level life sciences problems based on real research scenarios. About Turing: Based in San Francisco, California, Turing is the world's leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems.
About the role
Design graduate-level life sciences problems based on real research scenarios. About Turing:
Based in San Francisco, California, Turing is the world's leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in coding, reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L.
About the Role
We are seeking Life Sciences Subject Matter Experts to design advanced scientific-reasoning tasks for LifeSciBench. This is a non-coding role focused on biological knowledge, experimental interpretation, data analysis, and rigorous scientific reasoning.
You will create challenging, objectively defensible problems based on realistic research scenarios, experimental results, biological data, figures, sequences, structures, and literature-style evidence. Tasks should test synthesis and interpretation rather than factual recall and help distinguish advanced AI reasoning capabilities.
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. …
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