Senior Staff/Principal Software Engineer - GPU System Software, Onboard Execution

Zoox is building the world's most advanced self-driving hardware and software solution. The efficiency demands of such a system require an expert fine tuning of both the compute hardware architecture as well as the algorithms and middleware that runs on it to achieve maximum throughput at the most optimal power levels.

The Software Performance team keeps the main onboard computer healthy and performant to ensure that our on-bot autonomy software meets its latency targets while also accommodating its safety goals. We are responsible for the efficient allocation of onboard compute resources (CPU/GPU/Memory/accelerators) ensuring contention is minimized and the autonomy stack executes harmoniously.

As the Onboard Execution team leader within the Performance team, you will lead a highly specialized team of software performance engineers in aggressively optimizing on-bot software runtime. This is a high impact role that sits at the intersection of ML Platform, Core, and Autonomy Software - ensuring that efficient compute utilization is a central tenet emphasized across the organization.

Architect onboard GPU execution. Define how perception, prediction, and planning workloads share onboard GPUs, including scheduling, multi-tenancy, isolation, and deterministic latency under contention. Drive compute reduction. Own the multi-year roadmap to cut onboard compute per vehicle by setting latency and utilization budgets and landing the biggest levers across models, runtimes, middleware, and hardware in real releases. Shape hardware and platform direction. Guide next-generation compute decisions (GPU and SoC selection, topology, memory budgets) with workload models and performance projections that justify them, then lead performance bring-up of each new platform. Lead inference performance at scale. Set direction for the inference stack (TensorRT, CUDA, custom kernels, quantization) and partner with ML teams so models are designed for the hardware they ship on. Provide technical leadership. Lead and mentor senior engineers, align directors and VPs through design docs, and make the hard calls when performance, safety, and schedule conflict.

BS in computer science, computer engineering, or related field and 10+ years in systems or performance engineering, including leading GPU or accelerator architecture across teams, or building and leading a performance engineering team. Deep CUDA platform (programming model, runtime, and driver) and NVIDIA GPU microarchitecture expertise (Ampere through Blackwell, Orin, Thor). Proven record setting technical direction and delivering large, measurable end-to-end gains in latency, utilization, or cost. Working knowledge of ML inference optimization (TensorRT, XLA, Triton, quantization, fusion) and expert C++ on Linux. Ability to align ML, systems, hardware, and leadership on a shared technical plan.

Real-time or embedded GPU experience: autonomy, robotics, AR/VR, games, or HPC. Production GPU multi-tenancy (MPS, MIG, priority streams). New GPU architecture or platform bring-up, benchmarking methodology, or workload modeling and projections.