• Lead architecture, development, and validation of learned behavior models (e.g., driver mimicry, multi-agent interaction, behavior cloning, reinforcement learning) for highway and freight-truck autonomy. • Define and implement data strategies: collect, label, and curate large behavior datasets (in-vehicle, simulation, fleet logs) for training and evaluation. • Develop scalable model training pipelines, infrastructure, and tooling to iterate quickly on behavior models, from prototype to production deployment. • Design and track performance metrics, analyze model behavior, diagnose failure modes, and drive continuous improvement of learned behaviors in simulation and on-vehicle. • Work with simulation, scenario, and validation teams to embed learned behavior models into verification and validation frameworks, ensuring coverage across operational design domains (ODDs). • Mentor and lead mid and senior-level ML engineers, set technical direction, and drive best practices in learned behavior engineering across the team.