<div>Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in ML and CV to make autonomous driving as seamless as possible. </div><div><br /></div><div><b>The Opportunity</b></div><div>Are you excited to lead our ML Performance Optimization initiatives and make our Training and Inference platform that enables autonomous driving as fast and efficient as possible? You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and Advanced Hardware Engineering group and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox.</div><div><br /></div><div>We build and operate the base layer of ML tools, deep learning frameworks, and inference systems used by our applied research teams for in- and off-vehicle ML use cases. You will lead a team of strong software engineers and act as a force multiplier for our internal customers. This team has a lot of growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look <a href="https://zoox.com/autonomy/">here</a>.</div><h3>In this role, you will:</h3><ul><li>Develop and execute a strategic vision for the ML Performance Optimization team to unlock ML innovation in autonomous driving and rider experience. </li><li>Lead the design, implementation, and operation of cutting-edge ML Training and inference performance optimization techniques.</li><li>Collaborate closely with x-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.</li><li>Enable the engineers in the team to grow their careers by providing technical guidance and mentorship.</li></ul><h3>Qualifications</h3><ul><li>Strong experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training.</li><li>Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.</li><li>Experience using profiling tools like NVIDIAs Nsight or PyTorchs Profiler for identifying model training and serving bottlenecks.</li><li>Proficient in Python and C++</li><li>Experience with model compression techniques to reduce model size and improve performance.</li></ul><h3>Bonus Qualifications</h3><ul><li>10+ years of total experience, including 4+ years of working on large-scale model training or inference platforms.</li><li>Excellent leadership skills with a demonstrated ability to lead high-performing engineering teams.</li></ul><div><b>About Zoox</b></div><div>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.</div><div><br /></div><div><a href="https://www.linkedin.com/company/zoox-inc">Follow us on LinkedIn</a></div><div><br /></div><div><b>Accommodations</b></div><div>If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.</div><div><br /></div><div><i><b>A Final Note:</b></i></div><div><i>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.</i></div>