Onboard Developer Platform Software Engineer
Waymo · Mountain View, CA · Sys Intel and Machine Lrng (SQT)
About this role
Waymo is hiring a mid-level Platform Engineer in the software engineering function based in Mountain View, CA (hybrid). The posting calls out experience with Reinforcement Learning, Machine Learning 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
- Platform 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.
Waymo is in the process of hyper-scaling. We must enhance developer productivity of onboard engineers to enable the entire organization to quickly scale and address emerging challenges.
The goal of this team is to build the infrastructure, architecture, tooling, and platform necessary to accelerate development by identifying and addressing the critical needs and bottlenecks faced by onboard engineers.
In this hybrid role you will report to a Staff Software Engineer / Tech Lead Manager.
You will:
- Develop reliable, scalable, and maintainable systems to meet user needs, including accelerating large-scale simulation and eval, reinforcement learning-based fine-tuning pipeline, and analysis.