Zoox

Fully electric autonomous ride-hailing service platform

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

Systems Software EngineeringConfirmed live in the last 24 hours

Full-time$339k - $455k /yr

About the Job

Requirements

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

Responsibilities

Define how perception, prediction, and planning workloads share onboard GPUs, including scheduling, multi-tenancy, isolation, and deterministic latency under contention.
Own the multi-year roadmap to reduce onboard compute per vehicle by setting latency and utilization budgets and delivering major improvements across models, runtimes, middleware, and hardware in real releases.
Guide next-generation compute decisions, including GPU and system-on-chip selection, topology, and memory budgets, using workload models and performance projections to justify decisions; lead performance bring-up of each new platform.
Set direction for the inference stack, including TensorRT, CUDA, custom kernels, and quantization, and partner with machine learning teams so models are designed for the hardware on which they will run.
Lead and mentor senior engineers, align directors and vice presidents through design documents, and make decisions when performance, safety, and schedule conflict.

Desired Qualifications

Real-time or embedded GPU experience in autonomy, robotics, augmented or virtual reality, games, or high-performance computing.
Production GPU multi-tenancy experience, including MPS, MIG, or priority streams.
Experience with new GPU architecture or platform bring-up, benchmarking methodology, workload modeling, or projections.

Salary Range: 339000 to 455000 (Currency: USD) (Pay period: per-year-salary)

Zoox is building a purpose-built robotaxi from the ground up and running it on public roads today, with riders. Every perception, prediction, and planning decision runs on an onboard compute: a fixed-power, fixed-thermal, safety-critical, real-time system.

That makes GPU execution one of the most consequential engineering problems in the company. Each new model generation wants more compute. Each new vehicle generation needs to cost less, use less power, and stay safe. Closing that gap is not a kernel-tuning exercise. It takes an end-to-end technical vision for how the whole onboard AI workload is scheduled, shared, isolated, and sized across GPUs, and the authority to drive that vision across ML, autonomy, middleware, and hardware organizations.

You will join Zoox's Software Performance organization, which owns end-to-end performance of the onboard software stack: profiling and observability, GPU compute, and compute platform. You will work closely with Perception, Prediction, Planning, ML Infrastructure, and Hardware teams, and you will be a senior technical voice for onboard compute across the company.

We are looking for the technical leader who sets that vision. You will define how Zoox runs AI on the vehicle, and you will own the strategy for doing more with less compute on every vehicle we build next.

In this role, you will:

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.

Qualifications

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.

Bonus Qualifications

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.

About Zoox

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.

A Final Note:

You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

About the Company

Zoox

Fully electric autonomous ride-hailing service platform

Zoox designs and operates an autonomous ride-hailing service built around a purpose-built, fully electric vehicle designed specifically for autonomous operation. The vehicle uses advanced sensors, cameras, and AI to navigate complex urban environments, offering a four-passenger interior for comfort. Unlike competitors that retrofit existing cars, Zoox engineers both the vehicle and the service from the ground up, enabling optimized safety, efficiency, and passenger experience through integrated software that optimizes routing and reduces wait times. The company emphasizes safety through extensive testing and regulatory collaboration, and sustainability via electric mobility. The ultimate goal is to provide a smarter, safer, and more efficient urban transportation system that improves city life while reducing emissions.

Company size: 5,001-10,000
Company stage: Acquired
Total funding: $1.2B
Headquarters: Menlo Park, CA
Founded: 2014

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