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Zoox

Fully electric autonomous ride-hailing service platform

Engineering Manager - ML Performance Optimization

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeFoster City, CA, USA$276k - $343k /yrHybrid

About the Job

Requirements

  • At least 8 years of relevant experience, including at least 3 years of experience managing engineers.
  • Strong technical background in machine learning performance optimization, including distributed training strategies (data, tensor, and pipeline parallelism, FSDP/ZeRO), mixed-precision training, kernel-level optimization (CUDA, Triton), compiler stacks (torch.compile, XLA, TVM), quantization, and profiling and benchmarking across GPU and embedded accelerators.
  • Experience building user-friendly machine-learning infrastructure that enables large-scale model training and high-throughput, low-latency serving use cases.
  • Experience with training frameworks such as PyTorch and JAX, leveraging GPUs for distributed model training.
  • Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
  • A proven track record of cross-functional collaboration with research, product, hardware, and platform teams to align priorities, influence technical direction, and deliver measurable performance improvements across organizational boundaries.

Responsibilities

  • Develop and execute a strategic vision and roadmap for machine-learning training and inference performance optimization, ensuring scalability, reliability, and performance to support autonomous driving.
  • Lead the design, implementation, and operation of a robust and efficient machine-learning platform for training, validating, serving, optimizing, and monitoring machine-learning 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 through quantization, pruning, and distillation, and low-latency on-vehicle inference within strict real-time and compute budgets.
  • Attract, hire, and inspire a diverse engineering team, fostering a culture of innovation, collaboration, and excellence.
  • Collaborate with machine-learning researchers, software engineers, data engineers, and hardware engineers to define requirements and align on architectural decisions.
  • Provide engineers with career-growth opportunities and clear, timely feedback.

About the Company

Zoox

Fully electric autonomous ride-hailing service platform

Zoox designs and operates an autonomous ride-hailing service built around a purpose-built, fully electric vehicle designed specifically for autonomous operation. The vehicle uses advanced sensors, cameras, and AI to navigate complex urban environments, offering a four-passenger interior for comfort. Unlike competitors that retrofit existing cars, Zoox engineers both the vehicle and the service from the ground up, enabling optimized safety, efficiency, and passenger experience through integrated software that optimizes routing and reduces wait times. The company emphasizes safety through extensive testing and regulatory collaboration, and sustainability via electric mobility. The ultimate goal is to provide a smarter, safer, and more efficient urban transportation system that improves city life while reducing emissions.

Company size
5,001-10,000
Company stage
Acquired
Total funding
$1.2B
Headquarters
Menlo Park, CA
Founded
2014

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