Machine Learning Engineer - Perception

Core responsibilities:

• Design, develop, train and evaluate multi-sensor fusion based deep learning models to understand obstacles and environmental context
• Understand and curate real and synthetic datasets to improve our models
• Perform latency optimization and deploy models to our robot fleet
• Build a deep understanding of Perception gaps and behavioral issues around difficult obstacle types in order to help plan and prioritize our work
• Collaborate with Prediction/Planner team to deploy fully autonomous vehicles in environments with difficult and rare obstacles, extreme weather conditions, and complex driving scenarios

This role directly impacts the productivity, safety, and capabilities of Zoox's autonomous system by validating algorithms in real-world conditions, contributing to the development of fully autonomous vehicles that aim to provide the next generation of mobility-as-a-service in urban environments.

Required qualifications:

• MS or PhD in Computer Science, Machine Learning, or related technical field with 5+ years of industry experience
• Proficiency in Python and some knowledge in C++
• Deep Learning expertise
• Experience developing multi-sensor fusion algorithms for object detection, panoptic segmentation or object tracking
• Familiar with Transformer architecture

Bonus qualifications:

• Publications in top-tier conferences (CVPR, ICCV, RSS, ICRA)
• Experience with autonomous robotics systems
• Experience implementing 3D Gaussian Splatting

No specific benefits, compensation, or salary information mentioned in the job posting.

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market, aiming to provide the next generation of mobility-as-a-service in urban environments.