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.

Required Skills:

• Minimum 3+ years of overall experience
• Strong experience with at least one of the following languages: Go
• Proficiency with Git, Docker, and basic software pipeline setup.
• Ability to understand and navigate complex codebases.
• Comfortable running, modifying, and testing real-world projects locally.
• Experience contributing to or evaluating open-source projects is a plus.

Nice to Have:

• Previous participation in LLM research or evaluation projects.
• Experience building or testing developer tools or automation agents.

• 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.

• Strong experience with at least one of the following languages: Go
• Proficiency with Git, Docker, and basic software pipeline setup.
• Ability to understand and navigate complex codebases.
• Comfortable running, modifying, and testing real-world projects locally.

• Minimum 3+ years of overall experience
• Experience contributing to or evaluating open-source projects is a plus.
• Previous participation in LLM research or evaluation projects.
• Experience building or testing developer tools or automation agents.