Engineering Manager, ML Performance Optimization

Job description

[Zoox](https://www.linkedin.com/company/zoox-inc) is on a mission to reimagine transportation and build autonomous robotaxis from the ground up that are safe, reliable, clean, and enjoyable for everyone. With bidirectional driving capabilities and four-wheel steering, our vehicle allows us to maneuver through compact spaces and change directions without needing to reverse. We are at a critical inflection point as we scale our robotaxi deployment, and it is a great time to join Zoox and have a significant impact on executing our mission. Our growing ML performance engineering leadership team is looking for an Engineering Manager, ML Performance Optimization. The centralized ML performance team at Zoox plays a crucial role in enabling innovations across all our ML research teams to develop and deploy models across our robotaxi and cloud infrastructure and to advance cutting-edge training and inference optimization techniques. **The Opportunity** We are working on many interesting challenges to enable rapid experimentation and scale our multi-modal Foundation models and RL infrastructure, and ensure these models run efficiently on our vehicles, meeting our latency targets. You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation and our Advanced Hardware Engineering group, and have the opportunity to significantly push the boundaries of ML scaling and acceleration at Zoox. You will lead a team of strong ML performance engineers and this team has many growth opportunities as we expand our geofences at U.S markets and venture into new ML domains. If you want to learn more about our ML Infrastructure, [here](https://youtu.be/j-LpPv9vBvk?t=36) is one of our past talks at re:Invent. ### In this role, you will: - **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. ### Qualifications - 8+ years of relevant experience, including 3+ years of management experience managing engineers. - 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.