Senior Machine Learning Engineer, Multimodal Perception

Autonomy MLE role focused on bridging perception and planning to develop and release robust learned driving policies. The primary objective of this role is to train, evaluate, and transition production-ready decision-making models into real-world autonomous navigation systems.

In this hybrid role, you will report to the Technical Lead Manager of the Special Vehicle Compliance team.

Develop, evaluate, and release learned driving policies for complex navigation and yielding scenarios.

Work cross-functionally at the intersection of perception and planning, translating multi-modal perception outputs into robust behavioral actions.

Deploy learned models into closed-loop simulation environments, benchmark against strict safety metrics, and drive the transition of these models into production releases.

Advance the transition from rule-based heuristics to scalable, data-driven learned policies.

2–5+ years experience training and releasing ML models in autonomous driving, robotics, or complex spatial AI.

Hands-on experience working across both perception and planning stacks. Proficiency in learned driving policies (RL / imitation learning), PyTorch / JAX, closed-loop simulator evaluation, and a track record of releasing models to production.