We are sharing a specialised consulting opportunity for experienced Senior Software Engineers with strong expertise in Python, Java, Rust, C++, Go, TypeScript, algorithms, debugging, feature development, codebase refactoring, DevOps workflows, CI/CD, and performance optimisation to contribute to an advanced AI training and reinforcement-learning environment project.
Selected professionals will create reproducible reinforcement-learning environments that test advanced AI systems on realistic software-engineering workflows. These tasks will reflect common DevOps, CI/CD, debugging, and command-line engineering scenarios using tools such as Git, Docker, GDB, AddressSanitizer, FFmpeg, and related development utilities. Each environment will require reliable validation and a golden reference solution. No prior experience in AI is required.
Key Responsibilities
Reinforcement Learning Environment Development
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Design realistic software-engineering workflows for AI evaluation
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Create reproducible reinforcement-learning environments
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Develop scenarios that reflect practical engineering and debugging tasks
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Define clear task objectives, expected behaviour, and acceptance criteria
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Ensure environments accurately measure technical reasoning and execution
DevOps & CI/CD Workflows
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Develop tasks reflecting common DevOps and CI/CD workflows
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Design scenarios involving build, test, deployment, and automation processes
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Incorporate realistic development-tool interactions into evaluation environments
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Create workflows that require effective use of command-line utilities
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Ensure scenarios reflect practical engineering constraints and failure modes
Debugging & Troubleshooting
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Design and solve complex debugging scenarios
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Work with tools such as GDB, AddressSanitizer, and related diagnostics
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Identify root causes of software failures
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Develop maintainable and technically sound fixes
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Create environments that test structured troubleshooting and diagnostic reasoning
Software Development & Refactoring
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Develop tasks involving feature implementation and code modification
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Refactor existing codebases for maintainability and clarity
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Preserve intended behaviour while improving implementation quality
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Evaluate architectural and implementation trade-offs
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Apply strong software-engineering standards across environments
Performance Optimisation
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Create tasks involving software-performance bottlenecks
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Analyse algorithms, data structures, and implementation efficiency
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Improve scalability, latency, throughput, or resource utilisation
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Validate that performance improvements preserve correctness
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Document optimisation decisions and technical trade-offs clearly
CLI Tool & Engineering Workflow Design
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Build scenarios using common engineering tools such as Git and Docker
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Incorporate debugging, build, media-processing, and system utilities where relevant
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Design tasks requiring correct sequencing of technical actions
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Evaluate whether workflows are completed accurately and efficiently
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Ensure environments resemble realistic software-development processes
Verification & Golden Reference Solutions
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Create golden reference solutions for each evaluation environment
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Develop deterministic validation methods
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Define objective criteria for successful task completion
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Ensure verification distinguishes correct, incomplete, and incorrect solutions
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Maintain reproducibility across repeated environment executions
Ideal Profile
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Strong professional software-engineering experience
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Proficiency in one or more of C++, Python, Java, Go, TypeScript, or Rust
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Deep understanding of algorithms and data structures
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Strong performance-tuning and optimisation skills
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Demonstrated experience debugging complex software systems
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Strong background in feature implementation and codebase refactoring
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Experience working with DevOps or CI/CD workflows
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Familiarity with command-line engineering tools such as Git, Docker, GDB, AddressSanitizer, or comparable utilities
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Proven ability to develop maintainable and scalable software solutions
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Experience with large-scale or distributed codebases is highly valuable
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Strong code-review experience and familiarity with software best practices
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Excellent written and verbal technical communication skills
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Strong attention to detail
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Comfortable collaborating across cross-functional and remote teams
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Familiarity with modern AI or machine-learning systems is advantageous but not required
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No prior AI-training experience is required
Engagement Details
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Part-time independent contractor engagement
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Fully remote and open globally
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Expected commitment: approximately 15 hours per week
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Displayed compensation range: $100–$150/hour
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Actual compensation structure is output-based, with payment made per task that meets project specifications
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Task completion time may vary depending on individual experience and workflow
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Minimum weekly submission requirements apply; the source does not specify the exact number of required tasks
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Work will involve reinforcement-learning environment creation, DevOps and CI/CD workflows, debugging, command-line tools, feature implementation, codebase refactoring, performance optimisation, deterministic validation, and golden reference solutions
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Screening includes an approximately 30-minute AI interview
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A technical assessment may also be included before hiring-manager review
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Roles are typically filled within approximately 48 hours
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Selected experts are expected to begin initial tasks within approximately 24–48 hours after onboarding
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Project scope, workload, technical environments, 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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