About Turing
Turing is one of the world’s leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent.

About the Role
We’re looking for experienced, hands-on software engineers to help

evaluate and improve AI coding models .

Rather than primarily building production applications, you’ll work with coding agents across real-world repositories and assess the quality of their work. You’ll review generated code and agent behavior, determine whether solutions are technically correct, identify failure modes, and create the evaluation signals and feedback used to improve model performance.

Think of the coding agent as another engineer whose work you’re reviewing:

Can it understand the task? Did it choose the right approach? Is the resulting code correct, robust, and maintainable? Can you explain precisely where it succeeded or failed?

What You’ll Do

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 closely with researchers and engineers to translate qualitative judgment into scalable processes

Share clear, actionable findings with AI researchers and engineers

What We’re Looking For

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