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Turing is seeking experienced software engineers (tech lead level) to help build LLM evaluation datasets from public GitHub histories. You will lead hands-on work, automate environments with Docker, triage issues, and assess test coverage. The role involves running code locally to gauge LLM performance and collaborating with researchers on challenging repositories.
Remote, with opportunities to lead a team of junior engineers on cutting-edge AI projects and developer tools tasks.
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.