About the role
Zoox 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 still in the early stages of deploying our robotaxis, and it is a great time to join Zoox and have a significant impact on executing this mission.
Our growing Software Infrastructure engineering leadership team is looking for a Senior Engineering Manager, ML Platform. The centralized ML Platform team at Zoox plays a crucial role in accelerating innovation across all our Autonomy and Data Science teams by enabling them to develop and deploy models on our robotaxi and cloud infrastructure.
The Opportunity
We are continuing to push the boundaries of our ML capabilities by scaling our Vision Language Models (VLMs), Vision Language Action Models (VLAs), and World models, as well as our RL infrastructure, among other things. You will work across all ML teams within Zoox: Perception, Prediction, Planner, Simulation, and Collision Avoidance, building a cutting-edge distributed GPU training platform and enabling SOTA inference optimization techniques such as quantization, distillation, compression, and pruning.
You will lead a team of strong software engineers and managers and act as a force multiplier for our internal customers. This team has many growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our ML Infrastructure, here is one of our past talks at re:Invent.
In this role, you will:
Vision: Develop and execute a strategic vision for our ML platform, ensuring scalability, reliability, and a joyful platform UX.
Technical acumen: Oversee the design, implementation, and operation of a robust and efficient ML training and on-vehicle and cloud serving platform.
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 the engineers in the team to grow their careers by providing the right opportunities along with clear and timely feedback.
Qualifications
10+ years of relevant experience, including 4+ years of management experience managing other managers and engineers.
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 (Hopper, Blackwell, etc.) for distributed model training.
Experience with GPU-accelerated inference using TensorRT, vLLM, SGLang, Ray Serve, or similar frameworks.
