About the role
Anyone AI is recruiting skilled Backend Developers (Python and JavaScript/TypeScript; working knowledge of Java, C#, or Go) 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