Zoox

The Offline Driving Intelligence team is responsible for developing Foundation Models for prediction and planning, applying them both off-vehicle to provide ML capabilities to simulation and validation and on-vehicle to influence driving models. Our team collaborates closely with the Planner team to advance overall vehicle behavior. We also work closely with our Perception, Simulation, and Systems Engineering teams to accelerate our ability to validate our driving performance.

As a Prediction and Planning 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.

In this role, you will:

• Develop new deep learning models that use imitation learning and reinforcement learning to generate driving plans for our autonomous vehicle. You will also work on novel techniques to estimate the quality of those driving plans along the dimensions of safety, progress, comfort, compliance.
• Contribute to our large-scale machine learning infrastructure to discover new solutions and push the boundaries of the field
• Develop metrics and tools to analyze errors and understand improvements of our systems
• Collaborate with engineers on Perception, Planning, and Simulation to solve the overall Autonomous Driving problem in complex urban environments

Qualifications:

• MS, or PhD degree in computer science or related field with 5 or more years of industry experience
• Experience with training and deploying transformer-based model architectures and reinforcement learning techniques
• Experience with production Machine Learning pipelines: dataset creation, training frameworks, metrics pipelines
• Fluency in C++ or Fluency in Python with a basic understanding of C++
• Extensive experience with programming and algorithm design

Bonus Qualifications:

• Strong mathematics skills
• Prior experience with Prediction and/or Planning for autonomous vehicles or robotics
• Conference or Journal publications in Machine Learning or Robotics related venues