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
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Contribute expert-level code samples and development solutions in Python3, Java, Rust, Go, C++, TypeScript, or comparable languages
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Apply experience gained from real-world open-source or production codebases
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Implement robust functionality while considering scalability, maintainability, and software quality
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Demonstrate sound software engineering judgement across complex development tasks
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Produce solutions that can be independently reproduced and validated
Debugging & Feature Development
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Analyse, troubleshoot, and resolve complex software defects across diverse codebases
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Identify root causes of incorrect behaviour, regressions, and system failures
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Implement new features from requirements through validated delivery
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Diagnose and resolve performance bottlenecks and inefficient implementation patterns
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Address edge cases and ensure solutions remain reliable across relevant scenarios
Refactoring & Performance Optimisation
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Refactor legacy or complex code to improve clarity, maintainability, and long-term reliability
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Identify architectural or implementation weaknesses within existing systems
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Improve software performance while preserving functional correctness
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Evaluate trade-offs between development speed, scalability, complexity, and maintainability
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Modernise codebases where appropriate without introducing unnecessary regressions
AI Evaluation Environments & Reference Solutions
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Create reinforcement-learning environments that evaluate AI systems on realistic software engineering tasks
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Develop reproducible problem environments and corresponding golden reference solutions
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Design tasks involving bug fixing, feature implementation, codebase refactoring, and optimisation
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Document technical reasoning, implementation decisions, and verification methodology clearly
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Review and validate peer-contributed code and technical submissions for correctness and clarity
Ideal Profile
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Clear and demonstrable open-source contributions through GitHub, GitLab, or comparable public development profiles
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Significant hands-on expertise in at least one of Python3, Java, Rust, Go, C++, or TypeScript
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Deep understanding of algorithms, data structures, and software engineering fundamentals
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Proven ability to debug complex systems and resolve technically challenging software defects
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Strong experience implementing robust software features
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Experience with performance optimisation and technical bottleneck analysis
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Background in large-codebase refactoring or legacy-system modernisation is advantageous
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Track record of delivering meaningful technical contributions from conception through implementation
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Strong ability to reason about code correctness, maintainability, and system behaviour
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Excellent technical documentation and communication skills
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Ability to review other engineers' code and identify technical weaknesses precisely
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Interest in AI systems and technical evaluation is beneficial
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No prior experience in AI training is required
Engagement Details
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Part-time independent contractor engagement
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Fully remote
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Compensation: $50–$100/hour
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Expected commitment: approximately 15 hours per week
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Compensation is output-based, with payment made for tasks that meet project specifications
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Minimum weekly submission requirements apply
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Applicants must be able to demonstrate meaningful open-source contributions through a public GitHub, GitLab, or comparable profile
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Work will involve software engineering, reinforcement-learning environment creation, debugging, feature development, refactoring, optimisation, and reference-solution development
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The selection process may include screening questions, an approximately 30-minute AI interview, a technical assessment, and hiring-manager review
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Selected professionals should be prepared to begin their first tasks within approximately 24–48 hours of completing onboarding
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Roles are typically filled within approximately 48 hours
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Project scope, workload, task complexity, and evaluation standards may evolve depending on project requirements
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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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