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About the role
Pay: $60–$120/hour equivalent, paid per accepted task
Location: Global, fully remote
Job Type: Contractor
Time Commitment: 15+ hours per week
Schedule: Flexible
micro1 is looking for exceptional open-source contributors to support an AI-training project focused on complex, real-world technical workflows.
In this role, you will use your programming and domain expertise to create, solve, review, and validate challenging tasks in software engineering, machine learning, science, hardware, or engineering. Your work will help improve how advanced AI systems reason, write code, use technical tools, and complete long-horizon projects.
Prior AI-training experience is not required. We are primarily looking for candidates with strong, publicly verifiable technical work.
What You’ll Work On
Create realistic technical tasks based on professional, scientific, or engineering workflows.
Build reproducible task environments, reference solutions, tests, and objective evaluation criteria.
Review AI-generated work for correctness, completeness, and technical quality.
Diagnose model failures and explain why an approach or result is incorrect.
Required Qualifications
Strong programming ability, including practical proficiency with Python.
Meaningful contributions to open-source software or substantial original public projects.
Experience in at least one relevant area, including:
Software engineering
Machine learning or 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 Skill
Applicants should be able to provide publicly verifiable evidence such as:
Meaningful merged contributions to established repositories
Maintainer or core-contributor responsibilities
Substantial original repositories or technical tools
Scientific or engineering software contributions
Public code supporting published research
Contributions involving implementation, debugging, testing, performance, infrastructure, simulation, or data processing
Forks, tutorial-only repositories, superficial documentation edits, contribution counts, or AI-generated repositories alone are not sufficient.
Preferred Qualifications
Experience designing technical tests, benchmarks, evaluation tasks, or reproducible environments.
Experience reviewing code or technical contributions from others.
Competition results, publications, public technical writing, or other evidence of expertise.
Previous experience contributing to complex projects over an extended period.
Application Process
Submit your application and public technical profiles.
Complete an AI interview that includes technical and integrity checks.
Complete a hiring-manager review.
If selected, complete project onboarding and begin your first assigned task.
Compensation
Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. The time required for each task will vary based on its complexity and the expert’s workflow.
Originally posted on Himalayas
