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Turing is seeking experienced software engineers at tech-lead level to contribute to LLM evaluation datasets. You will triage issues, set up repositories with Docker, and evaluate test coverage.
You will work closely with researchers to identify hard problems for LLMs and may lead a small team of junior engineers on exciting AI projects. We value hands-on software engineering, environment automation, and the ability to navigate complex codebases.
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.