• Lead applied machine learning research focused on improving verification and validation of ML components used in robotics and autonomous driving systems. • Prototype research concepts into performant tools integrated into CI/CD and large-scale validation pipelines. • Advance ML research for open and closed loop simulation validation. • Develop scenario generation, coverage-guided testing, and rare-event discovery tooling. • Create robust metrics, predictors, uncertainty and Out-of-Distribution detection methods for autonomy ML systems. • Evaluate deep learning modules across perception, prediction, and planning in realistic sensor and traffic simulation. • Improve behavioral coverage and hazard-aligned metrics used in release readiness decision making. • Collaborate with Simulation, Safety, Systems Engineering, and cross-functional partners. • Author technical documentation, white papers, and contribute to validation methodology standards.