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Anyone AI Inc. is seeking skilled Full-Stack Developers (Python, JavaScript/TypeScript) to work on a project with a leading AI Lab. This is a remote, part-time consultant role with per-project compensation, flexible hours, and independent work.
You will design and implement multi-file tasks, write specs, and develop unit and integration tests, while reviewing peer submissions for quality and realism. Ideal candidates have 3–7 years of software experience, strong Python/JS/TS, English
Anyone AI is recruiting skilled Full-Stack Developers (Python, Javascript/TypeScript) to work on a project with a leading AI Lab.
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
Part-time, project-based expert evaluation work
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).
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
High-quality, production-realistic coding tasks
Complete and correct reference implementations
Robust test coverage and validation artifacts
Structured, actionable peer review feedback