Engineering Evaluators
Connect with expert software engineers, DevOps specialists, and system architects to evaluate AI models on complex technical challenges.
Software engineering is one of the most demanding domains for AI evaluation. Our evaluators bring deep production experience and are vetted for both technical depth and the ability to assess AI responses critically and consistently.
Key Evaluation Areas
Software Architecture
Design patterns, scalability trade-offs, system design, and long-term maintainability in production contexts.
Code Quality
Security implications, performance optimization, and best-practice review across languages and frameworks.
DevOps & Infrastructure
CI/CD pipelines, cloud platforms, containerization strategies, and observability tooling.
Algorithm Design
Data structures, complexity analysis, and optimization under real-world resource constraints.
Why Engineering Evals Are Critical
- AI must navigate architectural trade-offs with real production consequences — not just textbook answers
- Security vulnerabilities in code suggestions can be subtle, high-stakes, and easy to miss without expert review
- Performance characteristics vary widely across languages, runtimes, and deployment environments
- Edge cases and failure modes require lived engineering experience to catch reliably
Evaluator Requirements
- 5+ years of professional software development in production environments
- Demonstrated depth in a specialty — frontend, backend, systems, infrastructure, or security
- Track record of building and operating systems at meaningful scale
- Current familiarity with modern development practices, tooling, and standards
Ready to Contribute?
Join our network of engineering evaluators and help ensure AI models meet the bar that real engineers expect.
Apply as an Evaluator