Software Engineer - Python/Typescript

Turing•Chicago, IL

18h

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 to improve model performance. The role involves determining whether coding agents understand tasks, select appropriate approaches, and produce correct, robust, maintainable code, then explaining clearly where they succeed or fail. The engineer will also support data-generation, collection, and evaluation workflows and collaborate with researchers and engineers to turn qualitative judgments into scalable processes. This is an independent contractor engagement of 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 candidates are asked to provide a specific hourly rate expectation. The evaluation process includes an AI interview, a practical code/AI evaluation exercise, and a hiring manager interview. The practical exercise focuses on reviewing and evaluating AI-generated code, not competitive programming or algorithm puzzles.

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.