Zoox is building the world's most advanced self-driving hardware and software solution. The efficiency demands of such a system require an expert fine tuning of both the compute hardware architecture as well as the algorithms and middleware that runs on it to achieve maximum throughput at the most optimal power levels.
Requirements
- 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).
- 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).
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.
Other
- BS in computer science or related field and 3+ years of experience.
- Proficiency with SQL, DataBricks, Looker, or other business intelligence tools.
- The listed range applies only to the base salary.
- Compensation will vary based on geographic location and level.
- Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance.
