Job Description

The Perception team at Zoox is fundamental to our autonomous vehicle technology, creating the understanding of the world for our self-driving robots. We enable safe and efficient navigation in complex environments through sophisticated detection, classification, and tracking systems. As an engineer in the Semantics and Foundation Model team, you will develop advanced multimodal large language models that enhance our vehicles' environmental understanding. You'll develop and fine-tune these models for off-vehicle analysis while also optimizing them for real-time performance on our robotaxi platform, ensuring they can efficiently identify hazards and interpret driving restrictions with minimal latency. Working alongside world-class engineers and researchers, you'll leverage premium sensor data and cutting-edge infrastructure to validate your algorithms in real-world conditions, directly impacting the safety and capability of Zoox's autonomous system.

In this role, you will:

• Lead the development of multimodal large language models that enhance our robotaxis' understanding of complex urban environments
• Design and implement efficient model architectures, balancing performance and computational constraints through fine-tuning and distillation techniques
• Drive end-to-end ML solutions from research to production, utilizing Zoox's extensive data pipelines and infrastructure to improve autonomous driving capabilities
• Collaborate with perception, planning, and systems teams to integrate your models into the vehicle's decision-making pipeline
• Validate and optimize your solutions using real-world driving scenarios, directly contributing to the safety and reliability of Zoox's autonomous system

Qualifications

• MS or PhD in Computer Science, Machine Learning, or related technical field
• Demonstrated experience training and deploying perception and computer vision based models
• Experience building and maintaining ML training pipelines, including data preprocessing, model training, and evaluation
• Proficiency in Python and ML libraries (PyTorch, NumPy) demonstrated through professional or research projects
• Experience with model optimization techniques such as quantization, pruning, or knowledge distillation

Bonus Qualifications

• Proficiency in C++
• Publications in top-tier conferences (CVPR, ICCV, RSS, ICRA)
• Experience with autonomous robotics systems