Software Engineer (Planner GPU Compute)

• 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

Benefits

• Paid parental leave
• Affinity groups and sports clubs
• Work from home opportunities
• Health insurance
• Our crew’s health and happiness is our first priority. We offer comprehensive health and mental health support, a wellbeing program, and unlimited and flexible paid time away
• We invest in our crew—and their families—for the long term. That includes generous family planning support, caregiver support, and strong cash compensation with great equity upside
• We look after our crew when they’re in the office too. Our famous food program is a great example, featuring a daily changing menu of local and sustainable dishes
• There’s a busy calendar of social events at Zoox, with more sports teams than you can count. And, of course, playing with robots is an important part of the job description- Experience in development, debugging, and profiling of complex multiprocess systems (e.g., robotic systems, game engines)
• Strong knowledge of C++ and experience in large code bases, comfortable in Linux development environments
• Strong knowledge of CUDA as applied to recent GPU microarchitectures (e.g., Ampere, Blackwell) and experience debugging/optimizing GPU kernels using tools like Nsight
• BS in computer science or related field and 6+ years of experience
• GPU kernel development experience