<div>The Offline Driving Intelligence (ODIN) team at Zoox is leveraging the latest in AI to craft algorithms that understand the world. We leverage large models first offline and we devise a path of impact into our self-driving robot, enabling safe and efficient navigation in complex environments.</div><div><br /></div><div>As an engineer in the ODIN team, you will develop advanced multimodal large language models that enhance environmental understanding. Youll develop and fine-tune these models for off-vehicle analysis while working with the onboard team to deliver impact in our robotaxi platform, ensuring they can efficiently identify hazards and interpret driving restrictions with minimal latency. Working alongside world-class engineers and researchers, youll leverage premium sensor data and cutting-edge infrastructure to validate your algorithms in real-world conditions, directly impacting productivity, safety and the capability of Zooxs autonomous system.</div><h3>In this role, you will: </h3><ul><li>Lead the development of multimodal large language models that enhance our robotaxis understanding of complex urban environments</li><li>Design effective model architectures and sophisticated training techniques, leveraging all the inputs from our sensor stack and the overall large scale data we have at Zoox.</li><li>Drive end-to-end ML solutions from research to production, utilizing Zooxs extensive data pipelines and infrastructure to improve autonomous driving capabilities</li><li>Collaborate with perception, planning, safety, and systems teams to integrate your models into the vehicles decision-making pipeline</li><li>Validate and optimize your solutions using real-world driving scenarios, directly contributing to the safety and reliability of Zooxs autonomous system</li></ul><h3>Qualifications: </h3><ul><li>MS or PhD in Computer Science, Machine Learning, or related technical field</li><li>Demonstrated experience training and deploying large language models (LLMs)</li><li>Experience building and maintaining ML training pipelines, including data preprocessing, model training, and evaluation</li><li>Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects</li><li>Experience training with large scale datasets (e.g. tens of millions of videos)</li></ul><h3>Bonus Qualifications:</h3><ul><li>Publications in top-tier conferences (CVPR, ICCV, RSS, ICRA)</li><li>Experience with autonomous robotics systems</li></ul>