Develop and deploy multi-sensor fusion deep learning models for fully autonomous vehicles, addressing complex obstacles and challenges in various driving environments.
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
5 years of industry experience or more
Proficiency in Python and some knowledge in C++
Deep Learning expertise, preferably with panoptic segmentation experience
Experience developing multi-sensor fusion algorithms for object detection, panoptic segmentation or object tracking
Familiar with Transformer architecture
Technical leadership experience with software or machine learning teams
TensorRT or CUDA experience
Experience of 3DGS for 3D reconstruction or novel view synthesis
Zoox is building a fully autonomous vehicle fleet from the ground up, coupled with the ecosystem necessary to launch this technology into urban environments. By integrating robotics, machine learning, and innovative design, Zoox is paving the way for a new era of mobility-as-a-service.
Please mention you found this job on AI Jobs. It helps us get more startups to hire on our site. Thanks and good luck!
Understand the required skills and qualifications, anticipate the questions you may be asked, and study well-prepared answers using our sample responses.
Machine Learning Engineer Q&A's