Engineering Manager (ML Performance Optimization)

Join Zoox, a cutting-edge autonomous vehicle company, as an Engineering Manager for ML Performance Optimization. In this role, you will lead a team of ML performance engineers, drive end-to-end performance optimization for large-scale model training and inference, and collaborate closely with cross-functional teams. You will have the opportunity to significantly push the boundaries of ML scaling and acceleration at Zoox.

Develop and execute a strategic vision and roadmap for ML Training and Inference Performance Optimization, ensuring scalability, reliability, and performance to support autonomous driving.

You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills

Responsibilities

• Lead the design, implementation, and operation of a robust and efficient ML platform to enable the 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, and low-latency on-vehicle inference.
• Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks
• Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases
• 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
• Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training

Requirements

• 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
• 8+ years of relevant experience, including 3+ years of management experience managing engineers

Pay

USD 276,000 - 343,000