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Open Source Contributor (GitHub)

$100–$150/h

Global, fully remote

Role overview

Pay: $100–$150/hour

Location: Global, fully remote

Job Type: Contractor (~15 hours per week)

Schedule: Flexible—you choose the hours and days you work, including weekends if needed


We are looking for highly skilled Open-Source Software Engineers to contribute to an AI training project involving software development, repository exploration, debugging, system architecture, and code evaluation.


The work involves creating, solving, reviewing, and validating challenging software-engineering tasks across front-end and back-end systems and helping with translating full coding builds from one particular language into another.


We are particularly interested in engineers who own or maintain open-source repositories or have made meaningful contributions to established projects. Contributions should demonstrate real engineering ability, not only documentation updates, dependency bumps, formatting changes, or minor typo corrections.


Candidates should be prepared to discuss specific repositories, pull requests, issues, and code they personally authored.

What You’ll Work On

  1. Explore and understand unfamiliar software repositories.
  2. Implement, modify, and debug front-end and back-end components.
  3. Trace data and execution across interfaces, services, libraries, APIs, databases, and external dependencies.
  4. Reproduce software issues and identify their underlying causes.
  5. Implement solutions that fit an existing project’s architecture and contribution standards.
  6. Write unit, integration, regression, and end-to-end tests.
  7. Review AI-generated patches for correctness, completeness, security, and maintainability.


Required Qualifications

  1. A public or otherwise verifiable GitHub profile.
  2. Meaningful ownership of or contributions to one or more open-source repositories.
  3. Strong software-engineering experience across front-end, back-end, full-stack, systems, developer tooling, infrastructure, or another substantial technical area.
  4. Experience with multiple programming languages.
  5. Ability to understand and modify unfamiliar codebases.


Relevant languages may include TypeScript, JavaScript, Python, Java, Go, Rust, C++, C#, Ruby, PHP, or other languages used in substantial open-source projects.


Process

  1. Apply to the role and complete the screening questions.
  2. Complete an AI interview of approximately 30 minutes.
  3. Complete the hiring manager review.

Compensation Structure

Compensation is output-based. Experts are paid per task that meets the project specifications. The time required to complete each task may vary depending on the expert’s experience and workflow.

Minimum submission requirements apply. Experts must submit a minimum number of tasks per week.

Start Timeline & Availability

We typically fill roles within 48 hours and are looking for experts who are ready to begin immediately. If selected, you will be expected to start your first tasks within 24–48 hours of completing onboarding.

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Sporterer
Future of work · September 2026

How big is the remote / hybrid opportunity?

Remote work is already huge — but the bigger story may be the hundreds of millions of jobs that could still move beyond geography.

Sporterer Insights · Approximate figures based on World Bank and ILO estimates
Global remote and hybrid work opportunity infographic

Remote work already connects millions of people with companies they would never have been able to work for a decade ago. But compared with the size of the global labour market, it may still be early.

The interesting number is not only how many people work remotely today — it is how many roles could plausibly become remote or hybrid next.

The chart above starts with roughly 3.3 billion people employed worldwide. Around 300 million jobs are already remote or hybrid, while an additional pool of roughly 250 million roles could potentially move in that direction as technology, workflows and company culture continue to change.

Skills are becoming more portable

This matters because more work can increasingly be matched to expertise rather than location. AI training and evaluation are a good example: companies need lawyers, engineers, designers, editors, researchers, finance professionals and many other specialists to help improve AI systems.

For workers, that creates access to projects outside their local market. For companies, it expands the talent pool from one city or country to the world.

Explore remote opportunities →