Software Engineer - Python/Typescript

Turing • Austin, TX

5h

About The Position

Turing is hiring an experienced, hands-on software engineer to evaluate and improve AI coding models. The role focuses on working with coding agents across real-world repositories rather than primarily building production applications. You will review generated code and agent behavior, assess technical correctness and quality, identify failure modes, and create evaluation signals and feedback to improve model performance.

The work includes developing and refining rubrics and evaluation criteria, producing evaluation and preference data, and building and maintaining pipelines and infrastructure for data generation, collection, and evaluation workflows. You will synthesize findings into clear updates and recommendations and collaborate with 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 market rate, and 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; 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 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.

What This Job Offers

• Career Level: Senior
• Education Level: No Education Listed