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 Engineering experts to contribute advanced technical expertise to data curation and evaluation for frontier AI models and labs. This senior reviewer role focuses on reviewing complex engineering problems, developing evaluation frameworks, and advising client teams assessing AI-generated technical 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 engineering disciplines.

What You’ll Do Day-to-Day

• Lead expert review of complex or ambiguous engineering problems and AI-generated responses.
• Evaluate technical accuracy across engineering analysis, design, modeling, and validation.
• Review content within your areas of expertise, such as mechanical, electrical, civil, chemical, aerospace, industrial, computer, or biomedical engineering.
• Assess engineering calculations, design specifications, experimental results, simulation outputs, and technical literature.
• Identify unsupported claims, incorrect assumptions, methodological errors, and gaps in technical reasoning.
• Evaluate proposed solutions for feasibility, safety, and alignment with applicable engineering standards.
• 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 technically supported commentary and document evaluation decisions.
• Provide specialized consultation within your research or industry domain.

Requirements

• PhD in Engineering or a closely related technical 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 engineering fundamentals, applied mathematics, and analytical methods relevant to your discipline.
• Specialized expertise in one or more engineering domains, such as mechanical systems, electrical circuits, structural engineering, chemical processes, control systems, manufacturing, robotics, or computational engineering.
• Experience with relevant experimental, design, or modeling methods, such as finite element analysis, computational fluid dynamics, circuit simulation, optimization, or systems modeling.
• Demonstrated research contributions through peer-reviewed publications, patents, or substantial technical projects.
• Ability to critically assess technical literature, experimental design, model assumptions, and data interpretation.
• Strong written communication skills and the ability to explain engineering 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, engineering applications of machine learning, AI-assisted design, or technical dataset development.
• Experience reviewing journal articles, research proposals, engineering designs, or technical reports.
• Experience teaching or mentoring graduate students, researchers, or engineering teams.
• Active involvement in professional societies such as IEEE, ASME, ASCE, AIChE, AIAA, or relevant specialist organizations.
• Familiarity with applicable engineering standards, verification and validation methods, and reproducibility practices.
• Professional Engineer licensure or equivalent professional credentials, where relevant to the specialization.

Performance Expectations

• Take senior reviewer responsibility for assigned review batches.
• Deliver accurate, reproducible, and technically defensible evaluations.
• Clearly document assumptions, calculations, evidence, uncertainty, and limitations.
• Maintain consistency and reliability when collaborating with other expert reviewers.
• Communicate constructively with client teams during advisory discussions.
• Meet agreed deadlines and follow established evaluation protocols.
• Disclose relevant equity, advisory, consulting, or employment relationships with organizations whose technologies or products may appear in project content.
• Disclosed