Senior Machine Learning Engineer, Multimodal Perception
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: Perception-focused MLE role dedicated to multi-modal sensor fusion, multi-task deep learning architectures for vehicle semantics and signal detection, dynamic high-resolution vision backbones, and large-scale automated data engines. In this hybrid role, you will report to the Technical Lead Manager of the Special Vehicle Compliance team.
Requirements
- 2–5+ years training and deploying production vision or multi-modal deep learning models.
- Experience with multi-modal sensor fusion (Camera + LiDAR + Audio), multi-task learning (MTL), transformer architectures, and PyTorch / JAX.
- Experience building large-scale data curation pipelines, active learning loops, and auto-labeling systems.
- Fluency with modern AI developer tools and foundation model workflows for fast prototyping.
Responsibilities
- Architect, train, and optimize multi-task deep learning models (PyTorch / JAX) across multi-modal sensor streams.
- Build automated data mining pipelines, active learning loops, hard-example curation, and auto-labeling systems.
- Develop high-resolution vision architectures and spatial-temporal transformer backbones.
- Leverage multimodal foundation models for automated data curation, synthetic edge-case generation, and failure triage.
- Profile and optimize models for efficient onboard accelerator inference.
Benefits
- discretionary annual bonus program
- equity incentive plan
- generous Company benefits program
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
