Staff Machine Learning Engineer, Prediction & Planning, System Architecture
Waymo · Mountain View, CA | San Francisco, CA | New York City, NY · Planner (7LU)
About this role
Waymo is hiring a staff-level Machine Learning Engineer based in Mountain View, CA | San Francisco, CA | New York City, NY. The posting calls out experience with Python, C, TensorFlow, PyTorch. Compensation is listed at $251,000–$310,000 per year.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- staff
- Track
- Tech leadership
- Employment
- Full-time
- Location
- Mountain View, CA | San Francisco, CA | New York City, NY
- Department
- Planner (7LU)
More roles at Waymo
Job description
from Waymo careersWaymo 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 Predictive Planning team (PrePlan) develops and deploys state-of-the-art machine learning solutions that predict the future state of the world and plan the Waymo Driver’s behavior. Our mission is to transform Waymo's unprecedented scale of driving data into robust, generalizable, and performant deep neural networks. These models enable the autonomous vehicle to navigate complex environments safely and efficiently.
The system architecture team handles the onboard contract of the model with the system, including kinematics, interfaces and representations. Our team’s mission is to work across the stack, building the best setup for the model to drive. We tackle this through projects in data, modeling, metrics, and the overall planner system.