Backend Developer - AI Trainer (Mexico)
$93,600–$166,400 year
Remote · Mexico
Job Summary
Backend Developer - AI Trainer (Mexico) engages in part-time, project-based expert evaluation work for Anyone AI. Responsible for designing and implementing multi-file coding tasks across bug fixing, feature development, refactoring, and testing; writing clear natural-language specifications and reference implementations; building and extending unit and integration test suites; reviewing peer submissions for correctness, clarity, and realism; identifying edge cases and potential failure modes; ensuring alignment between specifications, code, and expected outputs to produce high-quality, production-realistic coding tasks and robust validation artifacts; remote work with Mexico City area listing and fully remote options; proficiency in Python and JavaScript/TypeScript, plus Java/C#/Go knowledge; 3–7 years of software engineering experience; strong testing framework experience (pytest, Jest, JUnit, xUnit, Go testing); English proficiency.
Required Qualifications
- Advanced professional written proficiency in English
- 3–7 years of professional software engineering experience
- Strong proficiency in Python and JavaScript/TypeScript; working knowledge of Java, C#, or Go
- Backend or full-stack development experience in production systems
- Experience with testing frameworks (e.g., pytest, Jest, JUnit, xUnit, Go testing)
- Proven ability to debug and navigate large, multi-file codebases
- Experience with code reviews, refactoring, and production migrations
- Engagement: Part-time, project-based expert evaluation work
- Work Type: Remote
- Contributors will design and evaluate realistic software engineering tasks, including bug resolution, feature implementation, refactoring/migration, and test generation
- Responsibilities: Design and implement multi-file coding tasks across bug fixing, feature development, refactoring, and testing
- Write clear natural-language specifications and reference implementations
- Develop and extend unit and integration test suites
- Review peer-generated tasks for correctness, clarity, and realism
- Identify edge cases, ambiguities, and potential failure modes
- Ensure alignment between specifications, code, and expected outputs
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