Remote Software Engineer - Python/Typescript

Turing•Austin, TX

3d•Remote

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. You will review generated code and agent behavior, determine whether solutions are technically correct, identify failure modes, and create evaluation signals and feedback to improve model performance. The role also involves creating and refining evaluation criteria, producing evaluation and preference data, and building and maintaining infrastructure for data-generation, collection, and evaluation workflows. You will synthesize findings into clear updates and recommendations and collaborate with AI researchers and engineers to turn technical judgment into scalable processes. This is an independent contractor engagement expected to last approximately three months. The preferred availability is 40 hours per week, with at least six hours of Pacific Time overlap. The start is as soon as possible. Compensation is at market rate, and applicants 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.