Open Source Contributor

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