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.

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