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

Requirements

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

Benefits

Health insurance
Long-term care insurance
Long-term and short-term disability insurance
Life insurance
Paid time off (e.g. sick leave, vacation, bereavement)
Unpaid time off
Zoox Stock Appreciation Rights
Amazon RSUs

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonC++Deep LearningMulti-sensor fusion algorithmsObject detectionPanoptic segmentationObject trackingTransformer architecture3D Gaussian SplattingLatency optimization

Soft Skills

CollaborationProblem-solvingPrioritizationCommunicationAnalytical thinking

Certifications

MS in Computer SciencePhD in Computer ScienceMachine Learning certification