Software Engineer - Python/Typescript
About The Position
Turing is seeking experienced, hands-on software engineers to evaluate and improve AI coding models. Rather than primarily building production applications, the engineer will work with coding agents across real-world repositories and assess the quality of their work. This includes reviewing generated code and agent behavior, judging whether solutions are technically correct, identifying failure modes, and creating evaluation signals and feedback to improve model performance. The role involves evaluating whether coding agents understand tasks, choose appropriate approaches, and produce correct, robust, and maintainable code. The engineer will explain where an agent succeeded or failed and help translate qualitative technical judgment into scalable evaluation processes. Engagement is as an independent contractor for approximately three months, with a preference for 40 hours per week and at least six hours of Pacific Time overlap. The role is open to candidates in North America, LATAM, or India, and is expected to start as soon as possible. Compensation is market rate; applicants should 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; these are 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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What This Job Offers
Career Level
Senior
Education Level
No Education Listed
