Senior Software Engineer, Infrastructure

at  Waymo

Waymo is an autonomous driving technology company with a mission to make it safe and easy for people and things to get where they’re going. Since our start as the Google Self-Driving Car Project in 2009, Waymo has been focused on building the Waymo Driver—The World’s Most Experienced Driver™—to improve everyone’s access to mobility while saving thousands of lives now lost to traffic crashes. Our Waymo Driver powers Waymo One, our fully autonomous ride-hailing service, as well as Waymo Via, our trucking and local delivery service. To date, Waymo has driven over 20 million miles autonomously on public roads across 25 U.S. cities and conducted over 20 billion miles of simulation testing.
At Waymo, we are mission-driven and believe deeply in the opportunity of autonomous driving technology to improve mobility and make people’s lives better. We are united by purpose and responsibility (for our employees and riders alike). We are looking for kind, committed, employees who have integrity, dream big, work together as one team and create a sense of belonging for one another that is the foundation of our culture. We want each team member to feel welcomed and included in every step of our exciting journey.
The Planner Infrastructure team at Waymo builds software that runs on the self-driving car, in addition to tools & benchmarking for ensuring that our system is performant and reliable. We are looking for infrastructure engineers to lead and help us take our system to the next level. This will ensure the self-driving car can operate in environments like freeways and crowded city streets.

• Report into our Planner Infrastructure TLM
• Ensure that our software is architected for reliability and implemented using advanced technologies and engineering best practices.
• Conduct software testing and validation, including the use of simulation and static analyzers to ensure that there are no reliability problems in our software that runs on the road.
• Ensure that our system can produce safe driving plans in an allotted timing and compute budget (potentially for hundreds of people and cars around the self-driving car).
• Be able to react and change direction at super-human latencies when there are new hazards detected by our perception system.
• Ensure our machine learning models are performing well on GPU/TPU and other hardware accelerators.