Engineering Manager, ML Performance Optimization

Location: Foster City, California, United States; hybrid workplace

Company

Zoox develops autonomous robotaxis designed to provide safe, reliable, clean, and enjoyable transportation.

What you will do

• Define and execute the vision and roadmap for ML training and inference performance optimization.
• Lead the design, implementation, and operation of ML infrastructure for model training, validation, serving, optimization, and monitoring.
• Optimize distributed training efficiency, GPU utilization, memory and communication, model compression, and real-time on-vehicle inference.
• Build and mentor a diverse engineering team, including hiring and career development.
• Partner with ML research, software, data, hardware, and platform teams on requirements and architecture.

Requirements

• 8+ years of relevant experience, including 3+ years managing engineers.
• Strong experience with ML performance optimization, distributed training, mixed precision, CUDA, Triton, compiler stacks, quantization, profiling, and benchmarking.
• Experience building ML infrastructure for large-scale training and high-throughput, low-latency serving.
• Experience with PyTorch, JAX, GPUs, and distributed model training.
• Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
• Proven cross-functional collaboration with research, product, hardware, and platform teams.

Culture & Benefits

• Work in a mission-driven environment focused on autonomous transportation.
• Collaborate across engineering, research, product, hardware, and platform functions.
• Inclusive culture that values diverse perspectives and backgrounds.