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Anyone AI

Software Engineering AI Trainer (Spain)

Posted 3 months ago
$45 - $80 per hour
5-10 years experience
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AI Summary

Design and implement complex, multi-file coding tasks including bug fixes, feature development, and test suites. Review peer-generated tasks for accuracy, realism, and alignment with specifications.

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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