Domain Expert: Life Science and Biology

Review and evaluate AI-generated outputs in life sciences for accuracy.

About Turing:

Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality data, evaluations, and reinforcement learning environments that improve model capabilities in coding, reasoning, tool use, and multimodality. In coding, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across Financial Services, Life Sciences, Healthcare, Retail, Automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models.

Role Overview

We are seeking Life Science experts to contribute advanced scientific expertise to data curation and evaluation for frontier AI models and labs. This senior reviewer role focuses on reviewing complex life science problems, developing evaluation frameworks, and advising client teams assessing AI-generated scientific outputs.

The role is designed for experienced researchers, faculty, and industry specialists interested in applying their expertise to improve the accuracy, reasoning, and reliability of AI systems across the life sciences.

What You’ll Do Day-to-Day

• Lead expert review of complex or ambiguous life science problems and AI-generated responses.
• Evaluate scientific accuracy across biological mechanisms, experimental methods, and interpretations of research findings.
• Review content within your areas of expertise, such as molecular biology, cell biology, genetics, biochemistry, microbiology, immunology, neuroscience, physiology, ecology, or evolutionary biology.
• Assess interpretations of experimental results, biological datasets, computational models, and scientific literature.
• Identify unsupported claims, methodological errors, and gaps in scientific reasoning.
• Evaluate whether conclusions are supported by appropriate controls, statistical analyses, and available evidence.
• Facilitate structured resolution of scientific disagreements among reviewers.
• Develop evaluation frameworks, scoring rubrics, and reference answers.
• Provide expert guidance to client teams reviewing AI model outputs.
• Author scientifically supported commentary and document evaluation decisions.
• Provide specialized consultation within your research or industry domain.

Requirements

• PhD in Life Sciences, Biology, Biochemistry, Biotechnology, or a closely related discipline.
• Minimum three years of post-PhD research, teaching, or relevant industry experience.
• Current or recent experience in academic research, an industrial R&D environment, or a recognized research institution.
• Strong understanding of biological principles, experimental design, and analytical methods relevant to your discipline.
• Specialized expertise in one or more life science domains, such as molecular and cellular biology, genetics and genomics, microbiology, immunology, neuroscience, pharmacology, ecology, or computational biology.
• Experience with relevant experimental or computational methods, such as sequencing, microscopy, flow cytometry, biochemical assays, bioinformatics, or biological modeling.
• Demonstrated research contributions through peer-reviewed publications, patents, or substantial scientific projects.
• Ability to critically assess scientific literature, experimental controls, statistical methods, and data interpretation.
• Strong written communication skills and the ability to explain scientific reasoning clearly.
• Ability to lead and document scientific consensus.
• Disciplined adherence to project protocols, confidentiality requirements, and quality standards.

Preferred Qualifications

• Faculty appointment at the rank of Assistant Professor or above, or equivalent senior research

Skills: Data Science