Software Engineer - Python/Typescript

Turing•Austin, TX

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 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 technical correctness, identifying failure modes, and creating evaluation signals and feedback that help improve model performance. The role involves evaluating AI-generated code and solutions, examining agents’ tool usage and code changes, comparing model outputs, and explaining precisely why an approach succeeds or fails. The engineer will also create evaluation rubrics and preference data, support data-generation and evaluation infrastructure, synthesize findings into written recommendations, and collaborate with AI researchers and engineers to turn qualitative judgment into scalable processes. This is an independent contractor engagement for approximately three months. The role prefers availability of 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, and is expected to start as soon as possible. Compensation is market rate; candidates are asked to 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.
Availability of 40 hours per week is preferred, 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.