Why this role stands out
Leverage your software engineering expertise to shape the future of AI training in this remote, project-based role with a leading AI lab. You'll thrive if you possess strong Python and JavaScript skills and enjoy designing and evaluating complex coding challenges, offering you flexible work and the chance to contribute to groundbreaking AI development. Apply today to make a significant impact!
Quick overview
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
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