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

About the Role

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.

Responsibilities

• 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.