Machine Learning Engineer - Perception
Develops and trains generative AI models for sensor-level scenario generation using world models and radiance fields to enhance Zoox's autonomous vehicle perception stack. Requires MS/PhD, 5+ years experience, Python/C++, deep learning, and multi-sensor fusion expertise.
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 scenario
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
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