AI Trainer (ML Engineering) - Remote at YO AI Labs

About this role

Job Description: Open-Source Software Contributor & Technical Expert Role Type: Contractor Location: Global (Fully Remote) Time Commitment: 15+ Hours per Week Schedule: Flexible About the Role We are seeking exceptional open-source contributors and technical experts to support an AI training project focused on complex, real-world technical workflows. In this role, you will apply your programming and domain expertise to create, solve, review, and validate challenging technical tasks across software engineering, machine learning, science, hardware, and engineering. Your contributions will help improve how advanced AI systems reason, write code, use technical tools, and complete complex, long-term projects. Prior AI training experience is not required. We are primarily looking for candidates with strong programming skills and publicly verifiable technical contributions. Key
Create realistic technical tasks based on professional, scientific, and engineering workflows.
Build reproducible task environments, reference solutions, tests, and objective evaluation criteria.
Review AI-generated outputs for correctness, completeness, and technical quality.
Diagnose AI model failures and explain technical errors, limitations, and incorrect approaches.
Develop and validate challenging tasks that assess practical problem-solving abilities.
Apply domain expertise to evaluate technical solutions across relevant fields.
Required Skills and Qualifications
Strong programming skills, including practical proficiency in Python.
Meaningful contributions to open-source software or substantial original public projects.
Demonstrated expertise in at least one of the following areas:
Software Engineering
Machine Learning and Data Science
Scientific Computing
Hardware, Embedded Systems, or Electronics
Mechanical, Aerospace, Civil, Chemical, or Electrical Engineering
Developer Tools, Infrastructure, Automation, or Security


Public Proof of Technical Expertise Applicants must be able to provide publicly verifiable evidence of their technical work, such as:
Meaningful merged contributions to established repositories.
Maintainer or core-contributor .
Substantial original repositories or technical tools.
Contributions to scientific or engineering software.
Public code supporting published research.
Contributions involving implementation, debugging, testing, performance optimization, infrastructure, simulation, or data processing.
Note: Forks, tutorial-only repositories, superficial documentation edits, contribution counts, or AI-generated repositories alone are not considered sufficient evidence of technical expertise. Preferred Qualifications
Experience designing technical tests, benchmarks, evaluation tasks, or reproducible environments.
Experience reviewing code or technical contributions from other developers.
Competition results, scientific publications, public technical writing, or other evidence of professional expertise.
Previous experience contributing to complex technical projects over an extended period.
Application Process
Submit your application along with public technical profiles and project contributions.
Complete an AI interview that includes technical and integrity checks.
Undergo a Hiring Manager review.
If selected, complete project onboarding and begin your first assigned task.
Originally posted on Himalayas