- Robot type
- Autonomous Vehicle
- Location
- Mountain ViewCaliforniaUSA
- Job type
- Software
- Posted
- Jun 24, 2026
- Salary
- $251,000–$310,000 a year
Staff Software Engineer, Compute Reliability
Job description
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 Tech Lead Manager.
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…
Job responsibilities
- Drive the design and development of instrumentation and onboard architecture to proactively prevent, detect, and debug reliability issues across current and future Waymo compute platforms.
- Design and implement long-term strategies to systematically mitigate compute hardware reliability and numerical stability issues across heterogeneous platforms (CPU, TPU, GPU), creating scalable tools to automate…
- Lead cross-functional initiatives across Waymo, Alphabet, and external partners to architect proactive solutions that prevent issues originating from compilers (LLVM, XLA, JAX), optimization (AutoFDO), or model changes.
- Architect frameworks and diagnostics to proactively identify and eliminate complex software and firmware faults, including deadlocks, memory corruption, race conditions, memory leaks, or tail latency spikes.
- Influence the design and tooling of next-generation hardware platforms, serving as a technical authority to ensure a coherent, forward-looking reliability architecture.
Job requirements
- BS/MS in Comp Sci, EE, Robotics, Physics, Math, or related field (or equivalent experience).
- Experience in C++.
- Experience with CUDA/GPU/TPU acceleration and model reliability or optimization.
- Experience with writing GPU kernels and evaluation/debugging of GPU workloads.
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