Software Engineer, Labeling Infrastructure
Waymo · Mountain View, CA · Sys Intel and Machine Lrng (SQT)
About this role
Waymo is hiring a mid-level Infrastructure Engineer in the software engineering function based in Mountain View, CA (hybrid). The posting calls out experience with Python, TypeScript, React, Angular and roughly 3+ years of relevant work. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $170,000–$216,000 per year.
- Role
- Infrastructure Engineer
- Function
- software engineering
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Mountain View, CA
- Work mode
- Hybrid
- Experience
- 3+ years
- Education
- Bachelor's degree
- Department
- Sys Intel and Machine Lrng (SQT)
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 Labeling Platform Team creates data solutions to power groundbreaking research and development during all stages of the ML Lifecycle: pretraining, supervised fine-tuning, reinforcement learning, etc. The labeled data that the team produces is used to directly enhance and evaluate the Waymo Driver as well as the vast variety of models that power other parts of the business.
In this hybrid role you will report to a Technical Lead Manager/Staff Software Engineer.
You will:
- Build state of the art labeling infrastructure to enable production of labeled datasets.
- Improve the "data flywheel" end to end efficiency through automation and monitoring.