Senior Software Engineer – LLM Evaluation & Repository Validation
Overview
This contract role centers on building LLM evaluation datasets from public repository histories. You will triage GitHub issues, set up Docker environments, and run codebases on your own machine to score how well language models fix real bugs. The work targets popular repositories with 500+ stars and spans languages such as Python, JavaScript, and Go. The project uses a human-in-the-loop approach to expand task coverage across difficulty levels and programming languages.
What You'll Do6
- 1Triage GitHub issues from trending open-source libraries and select those worth turning into LLM evaluation tasks.
- 2Set up repository environments by writing Dockerfiles, installing dependencies, and configuring build steps.
- 3Assess unit test suites for coverage and quality, then flag gaps that weaken evaluation reliability.
- 4Check out, modify, and run real codebases to test how LLMs perform on bug-fixing tasks.
- 5Collaborate with researchers to identify repositories and issues that challenge LLMs.
- 6Lead a small team of junior engineers working on related project milestones.
Requirements6
- 1Strong command of at least one language from this set: Python, JavaScript, Java, Go, Rust, C/C++, C#, or Ruby.
- 2Solid working knowledge of Git, Docker, and standard build pipeline configuration.
- 3Ability to read and move through complex, unfamiliar codebases without hand-holding.
- 4Comfort with checking out, changing, and executing real-world projects on a local machine.
- 5Prior open-source contribution or evaluation experience is a plus.
- 6Exposure to LLM research, LLM evaluation, or developer tooling and automation agents is preferred.
Who Should Apply
This role suits a senior or tech lead engineer who enjoys digging into popular open-source repositories and turning messy GitHub issues into structured evaluation tasks. The role works less well for developers who prefer building greenfield products over maintaining and debugging existing code. Candidates often get screened out when they lack Docker or Git fluency, or when they cannot show experience with high-star repositories (500+ stars). A history of contributing to open-source projects or evaluating LLM outputs gives you a clear edge.
Location
Required Skills
Application Tip
Show specific examples of setting up Docker environments for a popular open-source repo (500+ stars) and triaging or resolving issues in that codebase. Mention the exact programming languages you used and any LLM evaluation or automation projects you have worked on.
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1Application received
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2ATS + eligibility screening
We check your profile against the role’s skills, seniority, and requirements.
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