• Design, build, and optimize ML models for localization, including learned pose estimation, map-matching, and sensor fusion pipelines using camera, LiDAR, and radar data. • Develop high-performance training and evaluation workflows, leveraging frameworks such as PyTorch, distributed training infrastructure, and large-scale datasets. • Collaborate with robotics and mapping engineers to integrate localization models into the autonomy stack, ensuring performance, stability, and real-time constraints are met. • Analyze failure cases, run ablations, improve model robustness, and drive rigorous experimentation to achieve production-level reliability. • Contribute to system design, code reviews, best practices, and documentation across the ML and autonomy organization.