**About the Project**
Turing is one of the world's fastest-growing AI companies, pushing the boundaries of AI-assisted software development. Our mission is to empower the next generation of AI systems to reason about and work with real-world software repositories.
Project Overview
We're building high-quality evaluation and training datasets to improve how Large Language Models (LLMs) interact with realistic software engineering tasks. A key focus of this project is curating verifiable software engineering challenges from public GitHub repository histories using a human-in-the-loop process.
Why This Role Is Unique
* Collaborate directly with AI researchers shaping the future of AI-powered software development.
* Work with high-impact open-source projects and evaluate how LLMs perform on real bugs, issues, and developer tasks.
* Influence dataset design that will train and benchmark next-gen LLMs.
* What does day-to-day look like:
* Review and compare 3–4 model-generated code responses for each task using a structured ranking system.
* Evaluate code diffs for correctness, code quality, style, and efficiency.
* Provide clear, detailed rationales explaining the reasoning behind each ranking decision.
* Maintain high consistency and objectivity across evaluations.
* Collaborate with the team to identify edge cases and ambiguities in model behavior.
Required Skills and Qualifications
* 7+ years of professional software engineering experience, ideally at top-tier product companies.
* Strong fundamentals in software design, coding best practices, and debugging.
* Excellent ability to assess code quality, correctness, and maintainability.
* Proficient with code review processes and reading diffs in real-world repositories.
* Exceptional written communication skills to articulate evaluation rationale clearly.
* Prior experience with LLM-generated code or evaluation work is a plus.
Engagement Details
* Commitment: ~20 hours/week.
* Type: Contractor (no medical/paid leave).
* Duration: 1 month (potential extensions based on performance and fit).