Software Engineer - Python/Typescript

Turing • New York, NY

1d

About The Position

Turing is seeking an experienced, hands-on software engineer to evaluate and improve AI coding models. Rather than primarily building production applications, the role involves working with coding agents across real-world software repositories and assessing the quality of their work. The engineer reviews generated code and agent behavior, determines whether solutions are technically correct, identifies failure modes, and creates evaluation signals and feedback to improve model performance.

The role also involves building and maintaining pipelines and infrastructure for data generation, collection, and evaluation workflows. The engineer will work closely with researchers and engineers to turn qualitative technical judgment into scalable processes and communicate clear, actionable findings. The engagement is an independent contractor position for approximately three months, with 40 hours per week preferred and at least six hours of Pacific Time overlap. The position is open to candidates in North America, LATAM, or India.

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.

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.

What This Job Offers

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