The work
You will assess code patches generated by large language models for open source repositories. Your reviews will help determine whether proposed changes are suitable and technically sound.
The role uses your experience maintaining repositories and reviewing contributor submissions. You will also provide feedback on work completed by other subject-matter experts.
- Evaluate AI-generated code patches for repositories you maintain.
- Write clear, detailed reasons for your quality and suitability assessments.
- Apply practical open source maintenance and contributor-review standards.
- Conduct peer reviews of work submitted by other experts.
What it pays and takes
This is a remote, part-time individual contractor role with a flexible schedule. Work must remain consistently active throughout the engagement.
- Pay: $150 to $300 per hour equivalent, paid per task that meets the project specifications.
- Time: About 15 hours per week, or less than 20 hours per week.
- Location: Remote, open to candidates across selected countries in the Americas, Europe, Asia, Africa, and the Middle East.
- Language: Fluent in English.
- Required: Experience maintaining an open source repository and reviewing contributor code.
- Required: Clear, verifiable open source contributions on GitHub or GitLab.
- Required: Strong software engineering judgment and concise, detailed technical writing.
- Helpful background: Relevant open source contributions using C++, Java, TypeScript, Rust, Go, Python, or C.
How it works
You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.
The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 8 screening questions.
About AI training work
AI training is the human work behind artificial intelligence, including reviewing code and rating model outputs. People with software engineering expertise help models produce more useful and reliable results, and specialist projects can pay more for that knowledge.