ML Performance Engineering Manager

Lever, Inc.; Foster City, Northern (CA, KY); Hybrid; USD 276,000 - 343,000; Full time

Benefits offered by this job

Zoox Stock Appreciation Rights

Amazon RSUs

Health insurance

Long-term care insurance

Long-term and short-term disability 보험

Job summary

Zoox is seeking an Engineering Manager, ML Performance Optimization to lead a team of ML performance engineers focused on training and inference scalability for autonomous driving. You will drive end-to-end optimization across distributed training, memory and communication, and model compression to meet real-time constraints.

You will hire and mentor engineers, collaborate with ML researchers, hardware and software teams, and shape the roadmap for scalable ML infrastructure across Zoox's

Qualifications

• 8+ years of relevant experience, including 3+ years of management experience.
• 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.

Responsibilities

• Vision: Develop and execute a strategic vision and roadmap for ML Training and Inference Performance Optimization, ensuring scalability, reliability, and performance to support autonomous driving.
• Technical acumen: 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.
• ML Performance Optimization: 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 that meets strict real-time and compute budgets.
• Hiring: Attract, hire, and inspire a diverse world-class engineering team, fostering a culture of innovation, collaboration, and excellence.
• Partnership: Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
• Mentorship: Enable engineers on the team to grow their careers by providing the right opportunities and clear, timely feedback.

Skills

Leadership

ML performance optimization

Distributed training

Cross-functional collaboration

Tools

CUDA

Triton

TensorRT

Torch.compile

XLA/TVM

Job description

Zoox is seeking an Engineering Manager, ML Performance Optimization to lead a team of ML performance engineers focused on training and inference scalability for autonomous driving. You will drive end-to-end optimization across distributed training, memory and communication, and model compression to meet real-time constraints.

You will hire and mentor engineers, collaborate with ML researchers, hardware and software teams, and shape the roadmap for scalable ML infrastructure across Zoox's