Stand out for this role — generate a tailored resume and cover letter in about a minute.
Anyone AI Inc. is seeking skilled Full-Stack Developers to contribute on a project with a leading AI lab. The role is part-time and project-based, focusing on evaluating realistic software tasks and tests.
Contributors will work remotely, design and assess coding scenarios, review peer submissions, and manage their own schedules with per-project compensation. This consultancy offers flexible engagement terms for talented engineers.
Anyone AI is recruiting skilled Full-Stack Developers 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