# Software Engineer - C++ GPU Performance

**Company:** [Zoox](https://hotfix.jobs/companies/zoox)
**Location:** Foster City, CA, Boston, MA, San Diego, CA, Seattle, WA
**Role:** Embedded Engineering
**Salary:** $168k – $239k/yr
**Experience:** 3+ years
**Skills:** C++, CUDA, Nsight, GPU, Linux, TensorRT, Xla, Avx, Opengl, Rocm
**Posted:** 2026-04-16

> Optimizes GPU-based algorithms for self-driving systems by instrumenting, monitoring, analyzing performance hotspots, and porting serial code to GPU. Requires 3+ years experience, strong C++/CUDA skills, BS in CS, and expertise in recent GPU architectures.

## 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 3+ 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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