About The Position
Turing is seeking experienced, hands-on software engineers to evaluate and improve AI coding models. Instead of primarily building production applications, engineers work with coding agents across real-world software repositories to assess their work. They review generated code and agent behavior, determine whether solutions are technically correct, identify failure modes, and create evaluation signals and feedback to improve model performance. The role involves judging whether a coding agent understands a task, selects an appropriate approach, and produces correct, robust, and maintainable code. Engineers explain clearly where an agent succeeded or failed and collaborate with researchers and engineers to turn qualitative technical judgment into scalable evaluation and data workflows. The engagement is an independent contractor role expected to last approximately three months, with a preferred availability of 40 hours per week and at least six hours of Pacific Time overlap. The position is open to candidates in North America, LATAM, or India and is expected to start as soon as possible.
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.
- Preferred availability of 40 hours per week and at least 6 hours of Pacific Time overlap.
- Location in North America, LATAM, or India.
Nice To Haves
- Experience with LLM evaluation, coding agents, RLHF, preference data, rubric design, or post-training.
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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What This Job Offers
Career Level
Senior
Education Level
No Education Listed
