Senior Software Engineer, Statistical Evaluation and Sampling
Waymo · Mountain View, CA | San Francisco, CA | New York City, NY · Simulation (7XW)
About this role
Waymo is hiring a senior-level Software Engineer based in Mountain View, CA | San Francisco, CA | New York City, NY. The posting calls out experience with Python, SQL, ETL, Machine Learning and roughly 5+ years of relevant work. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $204,000–$259,000 per year.
- Role
- Software Engineer
- Function
- software engineering
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Mountain View, CA | San Francisco, CA | New York City, NY
- Experience
- 5+ years
- Education
- Bachelor's degree
- Department
- Simulation (7XW)
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.
Waymo's Release Evaluation org ensures that each version of the Waymo Driver is safe before it hits the road. We build automated pipelines to solve the long tail of rare and exceptional scenarios in autonomous driving, looking for needles in a haystack under both time and resource constraints. Within Release Evaluation, the Sampling and Efficiency team applies importance sampling techniques and machine learning to maximize the statistical efficiency of these discovery pipelines.
You will:
- Develop importance sampling techniques that enable our evaluation pipelines to deliver better signals with fewer resources.