The work
You will help evaluate AI systems on realistic software engineering problems. The work focuses on open-source repositories, GitHub issues, code quality, and local testing.
You may also help researchers choose difficult repositories and issues and support junior engineers during project work.
- Analyze and triage GitHub issues across open-source libraries.
- Set up repositories using Docker and development environment automation.
- Evaluate unit test coverage and test quality.
- Run and modify codebases locally to assess LLM bug-fixing performance.
- Work with researchers to select repositories and issues for evaluation.
- Support junior engineers on collaborative project work when needed.
What it pays and takes
This is a part-time contractor role with a commitment of at least 20 hours per week. It is open to candidates in India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, and Mexico.
You should be comfortable working in English and have strong practical software engineering experience, especially with Python and local development environments.
- At least 3 years of overall engineering experience.
- Strong Python experience.
- Practical skill with Git, Docker, and software pipeline setup.
- Comfort navigating complex codebases and testing real-world projects locally.
- Experience contributing to or evaluating open-source projects is helpful.
- Prior LLM research or evaluation experience is helpful.
- Experience building or testing developer tools or automation agents is helpful.
- Familiarity with high-quality public GitHub repositories is helpful.
- Professional fluency in English.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site.
About AI training work
AI training work is the human side of building artificial intelligence. People prepare examples and review model behavior, including how well an AI system understands and fixes software problems.
Software engineers are needed for projects that require careful testing, code review, and judgment about real-world engineering quality.