Software Engineer, Sensor/Imaging
Waymo · Mountain View, CA | San Francisco, CA · Camera_ UX_ Positioning_ Domes (7LS)
About this role
Waymo is hiring a mid-level Software Engineer based in Mountain View, CA | San Francisco, CA. The posting calls out experience with Python, SQL, Computer Vision, Testing. Compensation is listed at $204,000–$259,000 per year.
- Role
- Software Engineer
- Function
- software engineering
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Mountain View, CA | San Francisco, CA
- Department
- Camera_ UX_ Positioning_ Domes (7LS)
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.
Hardware Engineering is an innovative and collaborative group of electrical, mechanical, reliability, software and vehicle engineers. We design, build, and perfect the products which are the eyes and ears of Waymo's autonomous driving technology, and integrate those products into vehicle platforms. We're seeking curious and talented teammates to keep us moving in the right direction.
This role follows a hybrid work schedule, and you will report to the Tech Lead Manager of the Sensor Software Team.
You will:
- Report into Sensor organization
- Collaborate closely with multidisciplinary teams—including Sensor, Signal Processing, Perception, and System Engineering—to drive hardware/software co-optimization, providing concrete data and analysis across guide design trade-offs and component selection