We are looking for someone who has personally gone through the hard parts: post-training LLMs, designing tool-use environments, building reward models or verifiers, creating evaluations that catch real failures, shipping reinforced models into production, and leading strong machine learning engineers through that process. Our agents must learn to work inside real enterprise software: querying state, proposing actions, invoking APIs, respecting constraints, handling exceptions, escalating uncertainty, and collaborating with human operators.