Agent Evaluation and Self-Improvement Engineer

Autonomous agents now do real work across NVIDIA, and every piece underneath them keeps changing: models, harnesses, tools, skills, and plugins. Evaluation is how a team knows whether a change made an agent better or quietly made it worse. We're looking for a principal engineer to set evaluation strategy at every layer, from individual skills and MCP servers to whole agents and products, and to design how agents use those results to improve themselves. Agents can already rewrite the skills and plugins they run on, then test whether the change helped. Making that loop trustworthy is the hard part. What does it take for an agent to get better on its own, and prove the improvement is real? We're building a system that answers that question for every agent at NVIDIA.…