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 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, determining whether solutions are technically correct, identifying failure modes, and creating evaluation signals and feedback that improve model performance. The role involves judging whether a coding agent understood a task and selected an appropriate approach, and whether its code is correct, robust, and maintainable. The engineer will explain precisely where the agent succeeded or failed, create evaluation and preference data, and help build scalable workflows for coding-model evaluation. The engagement is an independent contractor position lasting approximately three months, with 40 hours per week preferred and at least six hours of Pacific Time overlap. The start date is 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.
- Ability to provide a specific hourly rate expectation.
- Preferred availability of 40 hours per week, with at least 6 hours of Pacific Time overlap.
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
