Senior / Staff Machine Learning Engineer, Zoox

• 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++

Desirable

• This role in the Offline Driving Intelligence team is responsible for developing/learning behavior models for road users such as cars, bicycles, and pedestrians. These agents populate Zoox's simulations and must be indistinguishable from real road users, yet fully controllable: promptable into the rare, adversarial, safety-critical behaviors we need to test against
• This means the team’s models directly impact how fast Zoox can train, validate and ship its driving stack. Our team collaborates closely with Planner, Simulation and Validation teams to develop and validate our driving performance. As an ML Agents Machine Learning Engineer, you will work on the bleeding edge of the industry, developing novel machine learning pipelines and models to predict the behavior of other agents in the world and planning the best course of action for the ego vehicle
• 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