Software Engineering AI Trainer (Ecuador)

Job description

Anyone AI is recruiting skilled software engineers to work on a project with a leading AI lab. **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. Work includes both creating complex coding scenarios and reviewing peer submissions for quality and accuracy. This is a project-based consultant role. Consultants will be paid on a per-project basis; hourly rates are estimates based on anticipated completion time. Consultants control their own schedule, provide their own tools, and may simultaneously provide services to other vendors/employers (subject to those vendors’ allowances). **Responsibilities:** Contributors will: - 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 **Expected Outcomes:** - High-quality, production-realistic coding tasks - Complete and correct reference implementations - Robust test coverage and validation artifacts - Structured, actionable peer review feedback