About the role
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.
About the Team: The "Oracle" for Autonomous Driving
Our mission is to build the high-fidelity, platform-agnostic Ground Truth (GT) engine that powers the next generation of autonomous vehicle (AV) simulation and evaluation. We function as the "Oracle"—by reprocessing onboard logs offline using state-of-the-art ML Foundation Models, we generate semantic signals that significantly outperform real-time onboard software.
Since our inception in early 2025, we have moved from concept to a fully operational end-to-end ML solution. As we head into 2027, we are scaling this "Oracle" to become the backbone of all large-scale evaluation, enabling the business to test software at a level of rigor and realism previously thought impossible.
The Role
We are looking for a strategic technical leader to drive the ML perception architecture for Sim GroundTruth. This role sits at the critical intersection of cutting-edge perception ML research, foundation models, and massive-scale offline data engineering. As a Senior Staff Machine Learning Engineer, you will serve as the chief technical authority for our perception models, guiding and mentoring ML engineers to solve the "technical moat" of high-fidelity 3D perception and ML inference at a petabyte scale.
Key Responsibilities
- Scalable ML Architecture & Signal Expansion: Architect a modular, extensible ML framework capable of rapidly absorbing a growing list of complex signal requirements. You will lead the transition from specialized models to unified Foundation Model architectures that maintain "Oracle" quality while scaling to hundreds of new semantic categories.
- Strategic Ownership & Quality Leadership: Define the multi-year roadmap for signal quality, coverage, and reliability. Your mandate is to ensure our signals don’t just match onboard software—they set the industry benchmark for ground truth accuracy.
- ML Scale & Inference Efficiency: Lead the optimization of high-fidelity perception model inference and compute efficiency across massive, petabyte-scale datasets, collaborating with backend infrastructure leads to support rigorous, high-frequency evaluation.