Senior Machine Learning Engineer, Multimodal Perception

Waymo•Mountain View, CA

6d•Hybrid

About The Position

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. The Special Vehicle Compliance team develops the multi-modal perception, semantic reasoning, and driving intelligence that enables the autonomous vehicle to safely interact with high-stakes road actors. We are actively advancing our systems toward data-driven learned policies and end-to-end architectures, powered by large-scale closed-loop data engines. Role overview: 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.

Requirements

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.

Nice To Haves

Familiarity with Vision-Language-Action (VLA) models and World Models is a strong plus
Experience using foundation models and AI tools for scenario generation, evaluation analysis, and rapid experimentation.

Responsibilities

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.