Open Source Contributor

micro1 Inc.; United States; A distancia; USD 83.000 - 165.000; A tiempo parcial

Descripción de la vacante

micro1 Inc. is seeking exceptional open-source contributors to support an AI-training project focused on complex, real-world technical workflows. You will create, solve, review, and validate challenging tasks in software engineering, machine learning, science, hardware, or engineering.

Prior AI-training experience is not required; strong, publicly verifiable technical work is valued. You’ll work on building reproducible task environments, reference solutions, tests, and objective evaluation

Formación

• Strong programming ability with practical Python experience.
• Meaningful contributions to open-source software or substantial public projects.
• Experience in software engineering, ML, data science, or related domains.

Responsabilidades

• Create realistic technical tasks based on professional workflows.
• Build reproducible task environments, reference solutions, tests, and evaluation criteria.
• Review AI-generated work for correctness and quality.
• Diagnose model failures and explain why results are incorrect.

Conocimientos

Python

Open-source contributions

Public repositories

Descripción del empleo

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

1. Create realistic technical tasks based on professional, scientific, or engineering workflows.
2. Build reproducible task environments, reference solutions, tests, and objective evaluation criteria.
3. Review AI-generated work for correctness, completeness, and technical quality.
4. Diagnose model failures and explain why an approach or result is incorrect.

Required Qualifications

1. Strong programming ability, including practical proficiency with Python.
2. Meaningful contributions to open-source software or substantial original public projects.
3. Experience in at least one relevant area, including:
4. Software engineering
5. Machine learning or data science
6. Scientific computing
7. Hardware, embedded systems, or electronics
8. Mechanical, aerospace, civil, chemical, or electrical engineering
9. Developer tools, infrastructure, automation, or security

Public Proof of Skill

Applicants should be able to provide publicly verifiable evidence such as:

1. Meaningful merged contributions to established repositories
2. Maintainer or core-contributor responsibilities
3. Substantial original repositories or technical tools
4. Scientific or engineering software contributionsPublic code supporting published research
5. 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

1. Experience designing technical tests, benchmarks, evaluation tasks, or reproducible environments.
2. Experience reviewing code or technical contributions from others.
3. Competition results, publications, public technical writing, or other evidence of expertise.
4. Previous experience contributing to complex projects over an extended period.

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.