# Senior Software Engineer - C++ GPU Performance

**Company:** [Zoox](https://hotfix.jobs/companies/zoox)
**Location:** Foster City, CA, Seattle, WA, Boston, MA, San Diego, CA
**Role:** Embedded Engineering
**Salary:** $217k – $307k/yr
**Experience:** 7+ years
**Skills:** C++, CUDA, Nsight, GPU, Linux, TensorRT, Xla, Ptx, Avx, Rocm, Opengl, SQL, Databricks, Looker
**Posted:** 2026-04-16

> Senior GPU performance engineer optimizes GPU-based algorithms for self-driving systems, instruments monitoring tools, analyzes hotspots, and collaborates on middleware for efficient compute utilization. Requires 7+ years experience, strong C++/CUDA expertise, and BS in CS.

## Job Description

## Responsibilities
- Build real-time instrumentation for performance monitoring (CPU, GPU, latency, memory) and develop offline benchmarking frameworks, tools, and scripts to evaluate & analyze performance at scale in CI/vehicle, and establish budgets for next-gen architectures.
- Analyze performance metrics to identify GPU hotspots and root causes, and propose and co-implement actionable solutions with component teams.
- Support teams on bringing serial algorithms to the GPU to maximize compute utilization and improve overall latency.
- Work as part of the Core team to design a middleware framework that promotes by default efficient and performant code development by maximizing CPU and GPU.

## Qualifications
- BS in computer science or related field and 7+ years of experience.
- Strong knowledge of CUDA as applied to recent GPU microarchitectures (e.g., Ampere, Blackwell) and experience debugging/optimizing GPU kernels using tools like Nsight.
- Strong knowledge of C++ and experience in large code bases, comfortable in Linux development environments.
- Experience in development, debugging, and profiling of complex multiprocess systems (e.g., robotic systems, game engines).

## Bonus Qualifications
- Experience with GPU kernel development in a real-time environment, including PTX-level programming, CPU SIMD instructions (e.g., AVX intrinsics), and custom CUDA layers with frameworks like TensorRT & XLA.
- Hands-on work with ML model optimization (post-training quantization, layer pruning, etc) or hand-tuning GPU kernels (in OpenGL, CUDA, RocM or similar).
- Proficiency with SQL, DataBricks, Looker, or other business intelligence tools.

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