Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for human-like driving agents.
Work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort and realism.
Build generative behavior models (e.g. autoregressive, diffusion) that are conditionable on scenario intent — "cut off the ego vehicle," "jaywalk here" — for targeted stress-testing.
Leverage our compute, infrastructure and large corpus of data to push boundaries of the field.
Develop metrics and tools to analyze errors and understand improvements of our systems.
Collaborate with engineers on Perception, Planning, Simulation, and Validation to solve the overall Autonomous Driving problem.
PhD degree in computer science or related field and 4+ years of relevant professional experience or master's degree and 7+ years of relevant professional experience
Experience in one of the following: Planning, Prediction, Reinforcement Learning, Imitation Learning, generative modeling (diffusion, autoregressive models)
Experience with training and deploying transformer-based model architectures
Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
Fluency in Python ML frameworks and a basic understanding of C++
Top tier publications (NeurIPS, ICML, CVPR)
Experience with JAX
Job details are sourced from the employer's original posting.
Open job postingAbout the company
Zoox is an autonomous vehicle company developing technology to enable safe, efficient, and enjoyable mobility for everyone. They are building a fully autonomous, electric vehicle from the ground up, designed for urban environments.