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

Zoox•Boston, MA

1d•$339,000 - $455,000

About The Position

Zoox is seeking a senior technical leader for GPU system software and onboard execution within its Software Performance organization. The organization owns end-to-end performance of the onboard software stack, including profiling and observability, GPU compute, and the compute platform. The role works closely with Perception, Prediction, Planning, ML Infrastructure, and Hardware teams, and serves as a senior technical voice for onboard compute across the company. The engineer will define how AI workloads run on Zoox vehicles and set the strategy for improving compute efficiency across future vehicle generations. This includes architecting how perception, prediction, and planning workloads share onboard GPUs; setting scheduling, multi-tenancy, isolation, and deterministic-latency approaches; and leading a multi-year roadmap to reduce onboard compute per vehicle. The role also guides GPU and SoC selection, topology, memory budgets, and new-platform performance bring-up, while setting direction for the inference stack and working with ML teams to optimize models for deployed hardware. The position provides technical leadership across senior engineering teams and organizational leadership, including mentoring engineers, aligning directors and VPs through design documents, and making decisions when performance, safety, and schedule priorities conflict. The listed base salary range is $339,000–$455,000 per year. Compensation also includes Amazon RSUs and Zoox Stock Appreciation Rights; a sign-on bonus may be offered. Zoox offers benefits including paid and unpaid time off, health insurance, long-term care insurance, disability insurance, and life insurance.

Requirements

Bachelor’s degree in computer science, computer engineering, or a related field.
10+ 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.
Expertise in NVIDIA GPU microarchitecture, including Ampere through Blackwell, Orin, and Thor.
Proven record of setting technical direction and delivering large, measurable end-to-end gains in latency, utilization, or cost.
Working knowledge of ML inference optimization, including TensorRT, XLA, Triton, quantization, and fusion.
Expert C++ skills on Linux.
Ability to align ML, systems, hardware, and leadership stakeholders on a shared technical plan.

Nice To Haves

Real-time or embedded GPU experience in autonomy, robotics, AR/VR, games, or HPC.
Production GPU multi-tenancy experience, including MPS, MIG, or priority streams.
Experience with new GPU architecture or platform bring-up, benchmarking methodology, or workload modeling and projections.

Responsibilities

Architect onboard GPU execution, defining how perception, prediction, and planning workloads share GPUs, including scheduling, multi-tenancy, isolation, and deterministic latency under contention.
Own a multi-year roadmap to reduce onboard compute per vehicle by setting latency and utilization budgets and delivering improvements across models, runtimes, middleware, and hardware.
Guide next-generation compute decisions, including GPU and SoC selection, topology, and memory budgets, using workload models and performance projections.
Lead performance bring-up for each new compute platform.
Set direction for the inference stack, including TensorRT, CUDA, custom kernels, and quantization, and partner with ML teams to design models for their target hardware.
Lead and mentor senior engineers, align directors and VPs through design documents, and make decisions when performance, safety, and schedule conflict.