About the Opportunity
This opportunity is for experienced Senior Software Engineers to contribute to advanced AI research and evaluation projects focused on training and improving next-generation AI systems.
In this role, you'll apply your software engineering expertise to create and evaluate Reinforcement Learning environments that test AI models on complex software engineering tasks using Model Context Protocol (MCP) tools.
Tasks may involve bug fixing, feature implementation, codebase refactoring, performance optimization, and debugging.
No prior AI experience is required. Your software engineering expertise, problem-solving abilities, and experience working with complex codebases are the primary qualifications for success in this role.
Responsibilities
- Create and evaluate Reinforcement Learning environments that test AI models on realistic software engineering tasks.
- Develop tasks involving bug fixing, feature implementation, codebase refactoring, and performance optimization.
- Design reproducible environments, deterministic verification systems, and reference solutions for evaluating AI performance.
- Apply expertise in algorithms, data structures, software architecture, debugging, and performance tuning.
- Evaluate AI-generated code for correctness, maintainability, scalability, and performance.
- Identify bugs, edge cases, implementation failures, and architectural tradeoffs.
- Work with Model Context Protocol (MCP) tools and real-world software engineering workflows.
- Provide structured technical feedback to improve AI coding capabilities, reasoning, and software engineering performance.
Required Qualifications
- 2+ years of professional Software Engineering experience or equivalent industry experience.
- Proficiency in one or more programming languages, including C++, Python, Java, Go, TypeScript, or Rust.
- Strong understanding of algorithms, data structures, software engineering principles, and performance optimization.
- Demonstrated experience with debugging complex software issues and delivering maintainable solutions.
- Strong background in feature development, codebase refactoring, and software optimization.
- Ability to analyze complex engineering problems and identify bugs, edge cases, and performance bottlenecks.
- Excellent analytical, problem-solving, written, and verbal communication skills.
- Ability to work independently in a fully remote environment.
Preferred Qualifications
- Experience working with large-scale or distributed codebases.
- Familiarity with AI, machine learning, reinforcement learning, or AI coding systems.
- Experience with Model Context Protocol (MCP) or tool-based AI workflows.
- Background in code reviews, software quality assurance, and engineering best practices.
- Experience working with cloud platforms, distributed systems, or performance-critical applications.
- Experience collaborating with cross-functional or remote engineering teams.
Compensation
Competitive compensation of $100–$150/hour.
Output-based compensation tied to completed tasks.
Independent contractor engagement.
Approximately 15 hours per week, depending on project requirements.
Application Process
Apply to the role and complete the screening questions.
Complete an AI Interview (approximately 30 minutes).
Complete a Technical Assessment, if required.
Hiring Manager Review.
Start Timeline & Availability
The team typically aims to fill positions quickly.
Selected candidates should be prepared to begin tasks within 24–48 hours after completing onboarding, subject to project requirements and availability.