MCP Expert for Reinforcement Learning Environments
Overview
An remote contract role for an MCP Expert who will help train next generation AI systems by building reinforcement learning environments. You’ll design deterministic test setups that measure how models reason about software engineering tasks, fix bugs, add features, refactor code, and push performance. Your domain knowledge in software development takes center stage, with no prior AI experience required. You’ll work across real MCP servers and provide reproducible environments, verification data, and gold standards to gauge tool use and engineering ability. Expect flexible hours and the chance to contribute to multi-language codebases.
What You'll Do5
- 1Create reproducible Reinforcement Learning Environments that challenge a model's ability to solve software engineering problems using MCP tools
- 2Fix bugs and implement features in existing codebases to a high standard
- 3Refactor large sections of code to improve maintainability and readability while preserving behavior
- 4Tune performance to ensure scalable results across distributed tasks and tests
- 5Develop deterministic verification procedures and golden reference solutions to quantify model reasoning and tool usage
Requirements7
- 1Proficiency in C++, Python, Java, Go, TypeScript, or Rust
- 2Strong grasp of algorithms, data structures, and performance tuning
- 3Proven debugging experience for complex software and delivering maintainable fixes
- 4Experience with feature development and codebase refactoring
- 5Track record of optimizing software for performance and scalability
- 6Excellent written and verbal communication with meticulous attention to detail
- 7History of collaboration in cross-functional teams, preferably in remote settings
Who Should Apply
The ideal candidate brings deep software domain knowledge and can design reliable test environments for AI evaluation. This role suits someone comfortable with multi-language stacks and distributed codebases. It’s less suitable for those without hands-on experience in debugging large systems or who avoid remote, async collaboration. Common fit challenges include limited experience with performance optimization in real-world projects or a lack of track record in cross-team code reviews.
Salary Insight
Pay is task-based and varies with the complexity of each submission; the range is not disclosed here. Compensation is discussed during later stages after task evaluation.
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Highlight a concrete project where you built or improved a large, multi-language codebase and quantified performance gains or reliability improvements.
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1Application received
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