Software Engineering AI Trainer (Brazil)
Anyone Ai · Brazil
- Company
- Anyone Ai
- Work style
- Remote
- Employment
- Part Time
- Location
- Brazil
- Published
- 2026-06-22
- Salary stated
- USD 45–80 per hour
- Source checked
- 2026-10-24
- Listing source
- Ashby Job Postings API
About this role
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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