About the Projects: - 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. About the Role: - 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. Why Join Us? - 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. What does day-to-day look like: - Analyze and triage GitHub issues across trending open-source libraries. - Set up and configure code repositories, including Dockerization and environment setup. - Evaluating unit test coverage and quality. - Modify and run codebases locally to assess LLM performance in bug-fixing scenarios. - Collaborate with researchers to design and identify repositories and issues that are challenging for LLMs. - Opportunities to lead a team of junior engineers to collaborate on projects. Required Skills: - Minimum 3+ years of overall experience. - Strong experience in JavaScript / TypeScript. - Proficiency with Git, Docker, and basic software pipeline setup. - Ability to understand and navigate complex codebases. - Comfortable running, modifying, and testing