Remote 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 role involves working with coding agents across real-world repositories and assessing the quality of their work. The engineer reviews generated code and agent behavior, determines technical correctness, identifies failure modes, and creates evaluation signals and feedback used to improve model performance. The role requires judging whether coding agents understand tasks, choose appropriate approaches, and produce correct, robust, maintainable code, as well as explaining precisely where they succeed or fail. The engineer will also build and maintain pipelines and infrastructure for data-generation, collection, and evaluation workflows; synthesize findings into clear write-ups and recommendations; and collaborate with AI researchers and engineers to make qualitative judgments scalable. This is an independent contractor engagement for approximately three months. The role is listed for North America, LATAM, or India. Forty hours per week are preferred, with at least six hours of Pacific Time overlap. The start is as soon as possible. Compensation is market rate, and 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.
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 closely 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
