Employer description & source details
The AI Quality engineering team at Circle owns the foundation for measuring, diagnosing, and improving the quality of Circle's AI-powered features. This team focuses on building the infrastructure to measure, diagnose, and improve production AI systems, rather than ML research or model training. We're looking for a Lead Engineer to help us build out the evaluation frameworks, observability tooling, and diagnostic infrastructure that tell us whether our AI Agents are working well, where to improve them, and how to make them faster and more cost-efficient. This is a hands-on player-coach role. You’ll also lead and manage the AI Quality engineering team, with an ambitious and growing roadmap. If you're excited about making AI systems work reliably, efficiently, and at scale, this is for you. Build and own our evaluation infrastructure. Design the CI/CD pipelines, scorers, and datasets that tell us whether Circle's AI agents (planners, tool-callers, and sub-agents) are actually working, from a single tool call to a full multi-turn conversation. Diagnose exactly where quality breaks down. Trace failures across the agent pipeline including plan creation vs. execution, tool selection, tool trajectory in complex areas like workflows, site builder, and analytics. Turn what you find into prototypes that solve the issue or identify priorities for AI core engineering can help. Grow the datasets that make evaluation possible. Stand up annotation workflows and build out AI-generated and simulated conversations so we can cover more of the product faster than manual labeling alone. Run structured experiments across prompts, models, and the agent harness. Evaluate new and open-source models against our production baseline, build the framework we use to decide when to shift models, and c
Extracted skills: python