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Boston, Massachusetts, United States
Hybrid
Zoox made the world’s first purpose-built commercial robotaxi, combining advanced self-driving hardware and software. In order to ensure safe operation, low latency and consistent resource utilization are critical. The Planner Compute team is responsible for the performance of the largest single software component within the autonomy stack, the motion planner.
The Planner Compute team is looking for an expert in GPU performance. You will instrument, monitor, analyze and optimize GPU-based algorithms that are performance-critical for our solution. The scope for GPU usage ranges from machine learning inference to custom CUDA kernels written specifically for the motion planner.
Analyze performance metrics to identify GPU hotspots and optimizations
Contribute to the adaptation of our current planner to multiple GPU architectures with different resources
Optimize ML models for latency and GPU memory usage
Support engineers within Planner as a subject matter expert on CUDA & GPU performance
BS in computer science or related field and 6+ 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).
