Software Engineer - Python/Typescript

Turing•San Francisco, CA

16h

About The Position

Turing is hiring experienced, hands-on software engineers to evaluate and improve AI coding models. Rather than primarily building production applications, this role involves working with coding agents across real-world repositories and assessing the quality of their work. The engineer will review generated code and agent behavior, determine whether solutions are technically correct, identify failure modes, and create evaluation signals and feedback that help improve model performance. This includes judging whether an agent understood a task, selected an appropriate approach, and produced code that is correct, robust, and maintainable, then explaining where it succeeded or failed. The role includes creating high-quality evaluation and preference data, refining coding-task rubrics, and building and maintaining pipelines and infrastructure for data generation, collection, and evaluation workflows. The engineer will collaborate closely with researchers and engineers and communicate actionable findings through clear write-ups, updates, and recommendations. This is an independent contractor engagement for approximately three months. The preferred availability is 40 hours per week, with at least six hours of Pacific Time overlap. The position is open to candidates in North America, LATAM, or India. Compensation is market rate, and applicants are asked to provide a specific hourly rate expectation. 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.

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.