Senior Staff/Principal Software Engineer – GPU System Software

Zoox

. Architect onboard GPU execution, including scheduling, multi-tenancy, isolation, and deterministic latency under contention for perception, prediction, and planning workloads .

Posted 10/22/2026full-timeUnited StatesSenior💰 $339,000 - $455,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in GPU architecture and performance engineering, with a strong focus on CUDA, TensorRT, and multi-tenancy solutions. Proven ability to lead teams, set technical direction, and optimize ML inference for onboard compute systems.

Highest-signal resume keywords

CUDA Platform ExpertiseNVIDIA GPU Microarchitecture ExpertisePerformance Engineering LeadershipML Inference OptimizationC++ Programming on Linux

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GPU ArchitecturePerformance EngineeringCUDA ProgrammingC++ ProgrammingML Inference OptimizationQuantizationBenchmarking MethodologyWorkload ModelingReal-Time GPU ExperienceEmbedded GPU Experience

Soft Skills

LeadershipMentoringCollaborationDecision-MakingTechnical Alignment

Tools & Technologies

TensorRTXLATritonCustom KernelsMiddleware

Industry Keywords

Systems EngineeringPerformance EngineeringMulti-TenancyDeterministic LatencyOnboard Compute

Tech Stack

Tools & technologies

LinuxC++

About the role

Key responsibilities & impact

Architect onboard GPU execution, including scheduling, multi-tenancy, isolation, and deterministic latency under contention for perception, prediction, and planning workloads
Own the 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 hardware platform
Set direction for the inference stack, including TensorRT, CUDA, custom kernels, and quantization
Partner with ML teams so models are designed for their deployment hardware
Lead and mentor senior engineers
Align directors and VPs through design documents
Make decisions when performance, safety, and schedule conflict

Requirements

What you’ll need

BS in computer science, computer engineering, or related field
10+ years in systems or performance engineering
Experience leading GPU or accelerator architecture across teams, or building and leading a performance engineering team
Deep CUDA platform expertise, including programming model, runtime, and driver
NVIDIA GPU microarchitecture expertise, including Ampere through Blackwell, Orin, and 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, including TensorRT, XLA, Triton, quantization, and fusion
Expert C++ on Linux
Ability to align ML, systems, hardware, and leadership on a shared technical plan
Real-time or embedded GPU experience is a bonus
Production GPU multi-tenancy experience is a bonus
New GPU architecture or platform bring-up, benchmarking methodology, or workload modeling and projections is a bonus

Benefits

Comp & perks

Paid time off, including sick leave, vacation, and bereavement
Unpaid time off
Zoox Stock Appreciation Rights
Amazon Restricted Stock Units (RSUs)
Health insurance
Long-term care insurance
Long-term and short-term disability insurance
Life insurance
Sign-on bonus may be offered