Senior C# Software Engineer, LLM Evaluation, Repo Validation

You will help build and validate datasets for realistic software engineering problems. The work uses public GitHub repositories and repository histories, with human review to assess how language models handle real code and bug-fixing tasks.

• Analyze and triage issues across trending open-source libraries.
• Set up repositories and local development environments, including Dockerization and basic pipeline setup.
• Review unit-test coverage and judge test quality.
• Modify and run codebases locally to evaluate LLM performance in bug-fixing scenarios.
• Identify repositories and issues that create meaningful challenges for language models.
• Work with researchers on task and dataset design.
• Lead or coordinate junior engineers when project needs require additional support.

Pay details are not provided in this listing. This is an individual contractor assignment with a fully remote arrangement.

• Commitment: at least 20 hours per week, with options for 20, 30, or 40 hours.
• Schedule: four hours of daily overlap with PST.
• Location: open to candidates in India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Türkiye, and Mexico.
• Language: fluent in English.
• Experience: at least three years of professional software engineering experience.
• Strong experience with C#.
• Proficiency with Git, Docker, and basic software pipeline setup.
• Ability to navigate complex codebases and work comfortably with real-world projects locally.
• Sound judgment when reviewing GitHub issues, repository quality, test coverage, and expected software behavior.
• Helpful background includes open-source contributions or evaluation, LLM research or evaluation, and building or testing developer tools or automation agents.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training is the human work behind systems that learn from examples, including code review, response evaluation, and software task testing. People with software engineering expertise help assess whether model outputs are accurate, useful, and aligned with real-world behavior.