Senior Software Engineer LLM Evaluation

You will assess how large language models handle realistic software-engineering problems and help validate repositories used in AI training datasets. The work combines hands-on coding, repository review, testing, and structured evaluation.

Review the duties and qualifications carefully before applying.

• Analyze and triage issues in widely used open-source libraries.
• Configure repositories and development environments, including Docker-based setups.
• Review unit-test coverage and assess whether tests are useful and reliable.
• Run and modify codebases locally to evaluate model performance on bug-fixing tasks.
• Work with researchers to find repositories and issues that create meaningful challenges for language models.
• You may help coordinate junior engineers on project work.

• Pay: No rate is provided for this role.
• Schedule: Part-time contractor work requiring 20 or more hours per week.
• Location: Open worldwide.
• Language: Professional fluency in English.
• Experience level: Entry level.
• Programming: Strong experience in at least one of Python, JavaScript, Java, Go, Rust, C, C++, C#, or Ruby.
• Technical skills: Proficiency with Git, Docker, and basic software pipeline setup.
• You must be able to understand complex codebases and run, modify, and test real-world projects locally.
• Helpful background includes contributing to or evaluating open-source projects, especially well-maintained public repositories.
• Experience in LLM research or evaluation, developer tools, or automation agents is relevant. Familiarity with public repositories with 500 or more stars is also helpful.

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 reviewing code and rating model responses. People with programming expertise help make these evaluations accurate and useful, which is why specialist AI-training work can require strong technical skills.