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Zoox is building autonomous robotaxis from the ground up with bidirectional driving and four-wheel steering capabilities. The company is at a critical inflection point scaling robotaxi deployment and seeking an Engineering Manager to lead the ML Performance Optimization team.
The ML performance engineering team plays a crucial role enabling innovations across all ML research teams to develop and deploy models across robotaxi and cloud infrastructure, advancing cutting-edge training and inference optimization techniques.
In this role, you will lead a team of strong ML performance engineers with significant growth opportunities as Zoox expands geofences across U.S. markets and ventures into new ML domains. You will work across all ML teams including Perception, Prediction, Planner, Simulation, and Advanced Hardware Engineering.
Key responsibilities include:
- Develop and execute strategic vision and roadmap for ML Training and Inference Performance Optimization, ensuring scalability, reliability, and performance for autonomous driving
- Lead design, implementation, and operation of robust ML platform enabling training, validation, serving, optimization, and monitoring of ML models
- Drive end-to-end performance optimization for large-scale model training and inference, including distributed training efficiency, GPU utilization, memory and communication optimization, model compression (quantization, pruning, distillation), and low-latency on-vehicle inference meeting strict real-time and compute budgets
- Attract, hire, and inspire a diverse world-class engineering team, fostering innovation, collaboration, and excellence
- Collaborate closely with cross-functional teams including ML researchers, software engineers, data engineers, and hardware engineers to define requirements and align on architectural decisions
- Enable engineers on the team to grow their careers through right opportunities and clear, timely feedback
Requirements:
- 8+ years of relevant experience, including 3+ years of management experience managing engineers
- Strong technical background in ML performance optimization, such as distributed training strategies (data, tensor, pipeline parallelism, FSDP/ZeRO), mixed-precision training, kernel-level optimization (CUDA, Triton), compiler stacks (torch.compile, XLA, TVM), quantization, and profiling/benchmarking across GPU and embedded accelerators
- Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases
- Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training
- Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks
- Proven track record of extensive cross-functional collaboration, partnering with research, product, hardware, and platform teams to align priorities, influence technical direction, and deliver measurable performance improvements across organizational boundaries
