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Remote | GitHub Contributor — $50–$100/hour

September 19, 2026

We are sharing a specialised part-time consulting opportunity for experienced software engineers with strong open-source contributions and demonstrable GitHub or GitLab profiles to contribute to an advanced AI training and software engineering evaluation project.

Selected professionals will create reproducible reinforcement-learning environments designed to test advanced AI systems on realistic software engineering problems involving bug fixing, feature implementation, codebase refactoring, and performance optimisation. The work requires strong hands-on engineering expertise, high-quality public code contributions, and the ability to develop rigorous reference solutions and clearly document technical reasoning. No prior experience in AI is required.

Key Responsibilities

Software Engineering & Open-Source Contribution

Contribute expert-level code samples and development solutions in Python3, Java, Rust, Go, C++, TypeScript, or comparable languages
Apply experience gained from real-world open-source or production codebases
Implement robust functionality while considering scalability, maintainability, and software quality
Demonstrate sound software engineering judgement across complex development tasks
Produce solutions that can be independently reproduced and validated

Debugging & Feature Development

Analyse, troubleshoot, and resolve complex software defects across diverse codebases
Identify root causes of incorrect behaviour, regressions, and system failures
Implement new features from requirements through validated delivery
Diagnose and resolve performance bottlenecks and inefficient implementation patterns
Address edge cases and ensure solutions remain reliable across relevant scenarios

Refactoring & Performance Optimisation

Refactor legacy or complex code to improve clarity, maintainability, and long-term reliability
Identify architectural or implementation weaknesses within existing systems
Improve software performance while preserving functional correctness
Evaluate trade-offs between development speed, scalability, complexity, and maintainability
Modernise codebases where appropriate without introducing unnecessary regressions

AI Evaluation Environments & Reference Solutions

Create reinforcement-learning environments that evaluate AI systems on realistic software engineering tasks
Develop reproducible problem environments and corresponding golden reference solutions
Design tasks involving bug fixing, feature implementation, codebase refactoring, and optimisation
Document technical reasoning, implementation decisions, and verification methodology clearly
Review and validate peer-contributed code and technical submissions for correctness and clarity

Ideal Profile

Clear and demonstrable open-source contributions through GitHub, GitLab, or comparable public development profiles
Significant hands-on expertise in at least one of Python3, Java, Rust, Go, C++, or TypeScript
Deep understanding of algorithms, data structures, and software engineering fundamentals
Proven ability to debug complex systems and resolve technically challenging software defects
Strong experience implementing robust software features
Experience with performance optimisation and technical bottleneck analysis
Background in large-codebase refactoring or legacy-system modernisation is advantageous
Track record of delivering meaningful technical contributions from conception through implementation
Strong ability to reason about code correctness, maintainability, and system behaviour
Excellent technical documentation and communication skills
Ability to review other engineers' code and identify technical weaknesses precisely
Interest in AI systems and technical evaluation is beneficial
No prior experience in AI training is required

Engagement Details

Part-time independent contractor engagement
Fully remote
Compensation: $50–$100/hour
Expected commitment: approximately 15 hours per week
Compensation is output-based, with payment made for tasks that meet project specifications
Minimum weekly submission requirements apply
Applicants must be able to demonstrate meaningful open-source contributions through a public GitHub, GitLab, or comparable profile
Work will involve software engineering, reinforcement-learning environment creation, debugging, feature development, refactoring, optimisation, and reference-solution development
The selection process may include screening questions, an approximately 30-minute AI interview, a technical assessment, and hiring-manager review
Selected professionals should be prepared to begin their first tasks within approximately 24–48 hours of completing onboarding
Roles are typically filled within approximately 48 hours
Project scope, workload, task complexity, and evaluation standards may evolve depending on project requirements
Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy:

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Common Interview Questions And Answers

1. HOW DO YOU PLAN YOUR DAY?

This is what this question poses: When do you focus and start working seriously? What are the hours you work optimally? Are you a night owl? A morning bird? Remote teams can be made up of people working on different shifts and around the world, so you won't necessarily be stuck in the 9-5 schedule if it's not for you...

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This is a great question because it shows your comfort level with technology, which is very important for a remote worker because you will be working with technology over time...