• Deploy ML-based motion planning and control models onto vehicle platforms, ensuring performance under resource constraints. • Optimize models for inference speed, latency, and memory footprint without sacrificing accuracy or safety. • Collaborate with motion planning, controls, and perception teams to integrate ML components into the end-to-end autonomous driving stack. • Build scalable deployment infrastructure including evaluation pipelines, model packaging, benchmarking, and automated validation. • Validate model performance in both simulation and on-road testing, analyzing results and driving iterative improvements. • Maintain production-quality code in C++ and Python