Full-Stack Developer
Pubblicato il 29-10-2026 - Anyone AI in Torino
Anyone AI is recruiting skilled software engineers who also speak Italian, 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: Tempo parziale, 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 #J-18808-Ljbffr