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Zoox
Zoox
1,001 – 5,000 Employees
H1B Visa SponsorArtificial IntelligenceAutomotiveTransport
Zoox develops autonomous mobility technology and a purpose-built fleet for mobility-as-a-service. Its work brings together robotics, artificial intelligence, vehicle engineering, electric vehicles, and advanced sensing to create transportation designed to be safer, cleaner, and more enjoyable. The company is focused on reshaping how people experience personal mobility through fully autonomous vehicles built around the needs of riders.
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Engineering Manager, ML Performance Optimization at Zoox

Lead machine learning performance optimization for Zoox’s autonomous driving systems. Guide scalable training, GPU acceleration, and low-latency inference across robotics engineering teams.

USUnited StatesCACalifornia
$276,000 – $343,000 / year
HybridFull-timeSeniorLeadEnglish RequiredEngineering Manager
PyTorchRay

Description

  • Set the strategic direction and roadmap for machine learning training and inference optimization
  • Improve scalability, reliability, and performance for autonomous driving workloads
  • Lead an efficient ML platform spanning model training, validation, serving, optimization, and monitoring
  • Optimize large-scale model training and inference from end to end
  • Increase distributed training efficiency while improving GPU utilization, memory use, and communication
  • Use quantization, pruning, distillation, and other model compression methods
  • Deliver low-latency on-vehicle inference within strict real-time and compute constraints
  • Build and motivate a diverse engineering team through hiring and leadership
  • Partner with ML research, software, data, and hardware engineering teams
  • Establish requirements and guide architectural decisions
  • Mentor engineers through development opportunities and timely feedback

Requirements

  • At least 8 years of relevant experience, including 3 or more years managing engineering teams
  • Deep expertise in ML performance optimization, including distributed training, data, tensor and pipeline parallelism, FSDP/ZeRO, mixed precision, CUDA, Triton, torch.compile, XLA, TVM, quantization, and profiling across GPU and embedded accelerators
  • Experience building accessible ML infrastructure for large-scale training and high-throughput, low-latency serving
  • Experience using PyTorch or JAX with GPUs for distributed model training
  • Experience with GPU-accelerated inference through TensorRT, Ray Serve, or comparable frameworks
  • A strong record of cross-functional work with research, product, hardware, and platform teams
  • Ability to set priorities, shape technical direction, and deliver measurable performance gains across organizational boundaries

Benefits

  • Paid leave covering sick time, vacation, and bereavement
  • Unpaid leave
  • Zoox Stock Appreciation Rights
  • Amazon Restricted Stock Units (RSUs)
  • Health insurance
  • Long-term care insurance
  • Long- and short-term disability insurance
  • Life insurance
  • Potential sign-on bonus

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