This role sits at the intersection of production machine learning, LLM-powered systems, and product engineering, with a focus on building, evaluating, and iterating on assistant experiences that are useful, reliable, and grounded in real enterprise workflows. The ideal person is excited by shipping production systems, not pure research, and wants to help shape how Glean's assistant gets better over time through stronger signals, tighter feedback loops, and better end-to-end execution quality.