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 Materials 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 materials 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 in materials science and engineering.

What You’ll Do Day-to-Day

• Lead expert review of complex or ambiguous materials science problems and AI-generated responses.
• Evaluate scientific accuracy across materials structure, properties, processing, and performance.
• Review content involving metals, ceramics, polymers, composites, semiconductors, and advanced functional materials within your areas of expertise.
• Assess interpretations of experimental results, characterization data, computational models, and scientific literature.
• Identify unsupported claims, methodological errors, and gaps in scientific reasoning.
• Facilitate structured resolution of technical 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 Materials Science, Materials Engineering, Metallurgy, 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 materials thermodynamics, kinetics, phase transformations, microstructure, and structure–property relationships.
• Specialized expertise in one or more areas, such as physical metallurgy, polymers, ceramics, composites, electronic materials, biomaterials, energy materials, or computational materials science.
• Experience with relevant characterization or modeling methods, such as microscopy, spectroscopy, diffraction, mechanical testing, density functional theory, or molecular dynamics.
• Demonstrated research contributions through peer-reviewed publications, patents, or substantial technical projects.
• Ability to critically assess scientific literature, experimental design, and data interpretation.
• Strong written communication skills and the ability to explain scientific reasoning clearly.
• Ability to lead and document technical 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 or industry experience.
• Prior experience with AI evaluation, materials informatics, machine learning for materials discovery, or scientific dataset development.
• Experience reviewing journal articles, research proposals, or technical reports.
• Experience teaching or mentoring graduate students, researchers, or engineering teams.
• Active involvement in professional societies such as MRS, TMS, ASM International, or relevant