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Turing is seeking experienced software engineers for a remote contractor role to help build LLM evaluation datasets and assess real code behavior. You will triage, set up environments with Docker, and evaluate unit test coverage across public repositories.
The role involves hands-on coding, environment automation, and collaboration with researchers to pick challenging repositories for LLM evaluation. 3+ years of experience and Python skills are required.
we are building LLM evaluation and training datasets to train LLM to work on realistic software engineering problems. One of our approaches, in this project, is to build verifiable SWE tasks based on public repository histories in a synthetic approach with human-in-the-loop; while expanding the dataset coverage to different types of tasks in terms of programming language, difficulty level, and etc.
We are looking for experienced software engineers (tech lead level) who are familiar with high-quality public GitHub repositories and can contribute to this project. This role involves hands‑on software engineering work, including development environment automation, issue triaging, and evaluating test coverage and quality.
Turing is one of the world’s fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. You’ll be at the forefront of evaluating how LLMs interact with real code, influencing the future of AI‑assisted software development. This is a unique opportunity to blend practical software engineering with AI research.