Software Engineering AI Trainer

Anyone AI is recruiting skilled software engineers to work on a project with a leading AI lab.

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

Requirements

• 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

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 27 allowances).

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