Senior Software Engineer, Onboard Software Integrity Infrastructure

Waymo 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 Planner/Perception Reliability team’s goal is to build out architectures, tools, and workflows to prevent, identify, and guide fixes of reliability and software integrity issues. 

We focus the organization on reliability and are accountable for the onboard software health while enabling high development velocity.

In this hybrid role you will report to a Staff Software Engineer / Tech Lead Manager.

You will:

• Architect the backbone of onboard software integrity: build the critical infrastructure and tooling ecosystem that ensures onboard software is robust and reliable by design


• Develop a deep understanding of onboard system architecture, focusing on aspects such as non-determinism and numerical fidelity across hardware platforms in production and simulation


• Understand low-level differences in computation outputs, e.g., due to hardware differences, compiler changes, or large-scale cloud issues and their impact on evaluation vs. onroad behavior


• Automate measurement, develop metrics, and debugging tooling for computational stability across simulation and onboard platforms


• Collaborate across Waymo to streamline integrity workflows, ensuring business-critical operations run smoothly and safely at scale

You have:

• BS/MS in Comp Sci, EE, Robotics, Physics, Math, or related field (or equivalent experience).


• Experience in C++ or Python, building scalable pipelines and tooling. 


• Experience with models numerical fidelity and the impact of hardware on the computation outputs (e.g., TPU vs GPU differences).


• Understanding of fundamental statistical concepts and applying them in the real world.


• Experience in organizing chaos across organizational boundaries.

We prefer:

• MS or PhD in Computer Science, Robotics, similar technical field of study, or equivalent practical experience.


• Experience with autonomous vehicles (L4) or ADAS systems (L2/L3).


• Experience in software reliability space.
  

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