Software Engineer - Python/Typescript

Turing•Seattle, WA

16h

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 to improve model performance. This includes assessing whether agents understand tasks, choose appropriate approaches, and produce correct, robust, maintainable code, then clearly explaining where they succeeded or failed. The role also involves creating and refining evaluation rubrics, producing evaluation and preference data, and building and maintaining infrastructure for data-generation, collection, and evaluation workflows. The engineer will synthesize findings into clear recommendations and collaborate with AI researchers and engineers to turn qualitative judgment into scalable processes. This is an independent contractor engagement of approximately three months. The preferred availability is 40 hours per week, with at least six hours of overlap with Pacific Time. The role is open to candidates in North America, LATAM, or India, and is expected to start as soon as possible. Compensation is at market rate; candidates 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.

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.