About The Position

Turing is hiring an experienced, hands-on software engineer to evaluate and improve AI coding models. The role focuses on working with coding agents across real-world repositories and assessing the quality of their work, rather than primarily building production applications. The engineer reviews generated code and agent behavior, evaluates technical correctness and quality, identifies failure modes, and creates evaluation signals and feedback to improve model performance. The role also involves producing evaluation and preference data, developing and maintaining infrastructure for data-generation and evaluation workflows, and collaborating with researchers and engineers to turn qualitative technical judgment into scalable processes. The engagement is an independent contractor position for approximately three months, with a preference for 40 hours per week and at least six hours of Pacific Time overlap. Start is as soon as possible. Compensation is market rate; applicants are asked to provide a specific hourly rate expectation.

Requirements

  • At least 5 years of hands-on software engineering experience.
  • Strong proficiency in Python, TypeScript/JavaScript, Go, or another major production language.
  • Experience working in substantial real-world codebases.
  • Strong code-review skills and technical judgment.
  • Ability to clearly explain why an implementation is correct, incorrect, or could be improved.
  • Strong written communication.
  • Experience using modern LLMs or AI coding tools.

Nice To Haves

  • Experience with LLM evaluation, coding agents, RLHF, preference data, rubric design, or post-training; this is a plus but not required.

Responsibilities

  • Evaluate AI-generated code and solutions across real-world software repositories.
  • Review agent behavior, tool usage, and code changes for correctness and quality.
  • Identify technical errors, weak approaches, and recurring model failure modes.
  • Compare model outputs and explain why one solution is better than another.
  • Create and refine rubrics and evaluation criteria for coding tasks.
  • Produce high-quality evaluation and preference data used to improve coding models.
  • Build and maintain pipelines and infrastructure supporting data generation, collection, and evaluation workflows.
  • Synthesize findings from data work into clear write-ups, updates, and recommendations for the team.
  • Collaborate with researchers and engineers to translate qualitative judgment into scalable processes.
  • Share clear, actionable findings with AI researchers and engineers.
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