This role involves applying domain expertise to help train next-generation AI systems by creating Reinforcement Learning Environments that test AI models' ability to solve complex software engineering problems using Model Context Protocol (MCP) tools.
Tasks include fixing bugs, implementing features, refactoring code, and optimizing performance while requiring agents to discover and reason over information from real MCP servers.
You will design reproducible environments, deterministic verification, and golden reference solutions that accurately measure both MCP tool use and software engineering ability.
The position is contractor-based (~15 hours/week), remote, and offers a flexible schedule where you pick hours and days.
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Requirements
Proficiency in C++, Python, JAVA, GoLang, Typescript, or Rust.
Deep understanding of algorithms, data structures, and performance tuning.
Demonstrated experience in debugging complex software issues and delivering maintainable solutions.
Strong background in feature development and codebase refactoring.
Proven ability to optimize software for performance and scalability.
Exceptional written and verbal communication skills, with a keen attention to detail.
Track record of success in collaborative, cross-functional teams, ideally in remote settings.
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Benefits
Remote work (Telecommute)
Flexible schedule — you pick the hours and days (including weekends if desired)
Contractor position (~15 hrs a week) allowing part-time/flexible engagement