Engineering Manager, ML Performance Optimization

Foster City · USD 276,000-343,000 / year · 3+ years experience · Full-time · Hybrid · Posted Oct 6, 2026

At a glance

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
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Accommodations
If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.
A Final Note:
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.
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The Engineering Manager for ML Performance Optimization will lead a team focused on enabling innovations across ML research teams to develop and deploy models for autonomous robotaxis.
This role involves driving end-to-end performance optimization for large-scale model training and inference, ensuring models meet strict real-time and compute budgets on-vehicle.
The manager will collaborate across Perception, Prediction, Planner, Simulation, and Advanced Hardware Engineering groups to push the boundaries of ML scaling and acceleration.

Develop and execute a strategic vision and roadmap for ML Training and Inference Performance Optimization.
Lead the design, implementation, and operation of a robust and efficient ML platform.
Drive end-to-end performance optimization for large-scale model training and inference.
Implement model compression techniques including quantization, pruning, and distillation.
Optimize low-latency on-vehicle inference to meet real-time and compute budgets.
Attract, hire, and inspire a diverse world-class engineering team.
Collaborate with ML researchers, software, data, and hardware engineers to define requirements and architectural decisions.
Provide mentorship and clear, timely feedback to enable team career growth.

8+ years of relevant experience.
3+ years of management experience managing engineers.
Strong technical background in ML performance optimization including distributed training strategies, mixed-precision training, and kernel-level optimization.
Experience with compiler stacks such as torch.compile, XLA, or TVM.
Experience building user-friendly ML Infrastructure for large-scale training and high-throughput, low-latency serving.
Experience with training frameworks like PyTorch or JAX leveraging GPUs.
Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
Proven track record of cross-functional collaboration with research, product, hardware, and platform teams.