Anyone Ai

Software Engineering AI Trainer (Mexico)

Ciudad de Mexico, CDMX, Mexico · Remote (country restricted) · Contract

USD 45–80/hour

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About the role

Description from the employer’s posting. Check the employer’s page for the current version.

EmployerAnyone Ai
LocationCiudad de Mexico, CDMX, Mexico
Work styleRemote (country restricted)
Employment typeContract
Employer posted3 months ago
Salary (as stated by the employer)USD 45 – 80 per hour

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.

Show full description

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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