Job description

On the Motion Planning team at Zoox, you’ll be dedicated to improving the driving behaviors of the robot on public roads. The Planner's job is to figure out where the robot is going and how to get there safely in situations as complex as those found in Downtown San Francisco or on the Las Vegas Strip. This is a cutting-edge problem, which requires teamwork and collaboration across a broad set of experts spanning Planner, Perception, Prediction, Operations, and more. Together, you will create the next generation of algorithmic improvements to expand our robot’s ability to drive in some of the most complex situations found on public roads. Safety is foundational at Zoox, so you will take a measured approach – develop a principled modification to the Planner, test in simulation and on closed courses, and team up with our safety drivers for on-road testing. In this role, you will: Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base. Use our extensive backend tooling to test your changes across many different driving situations at the press of a button. Lead on-vehicle drive reviews to generate actionable feedback on driving performance. Use this experience to help the team learn and make improvements. Attend conferences and survey literature to stay informed on the most recent developments in the fields of Robotics and Motion Planning. Write and file patents for novel technologies you develop. Qualifications Master's degree in Computer Science or related field and relevant experience Fluency in C++ Understanding of configuration spaces and a variety of planning techniques (A*, RRTs, PRMs, etc.) Demonstrated ability to create real-time motion planning algorithms Bonus Qualifications Significant contributions to geometric- and/or sampling-based planning algorithms Experience with autonomous robots and machine learning or data-driven development processes Experience with ROS, pose estimation, SLAM, probabilistic filtering, 3D data, or CUDA About Zoox Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team. Follow us on LinkedIn Accommodations If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter. A Final Note: You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

Requirements

Must-haves

Safety is foundational at Zoox, so you will take a measured approach – develop a principled modification to the Planner, test in simulation and on closed courses, and team up with our safety drivers for on-road testing.
Create principled algorithmic improvements to specific areas of driving behavior and implement those changes in our code base.
Use our extensive backend tooling to test your changes across many different driving situations at the press of a button.
Lead on-vehicle drive reviews to generate actionable feedback on driving performance. Use this experience to help the team learn and make improvements.
Attend conferences and survey literature to stay informed on the most recent developments in the fields of Robotics and Motion Planning.
Write and file patents for novel technologies you develop.
Master's degree in Computer Science or related field and relevant experience
Fluency in C++
Understanding of configuration spaces and a variety of planning techniques (A*, RRTs, PRMs, etc.)
Demonstrated ability to create real-time motion planning algorithms
Significant contributions to geometric- and/or sampling-based planning algorithms
Experience with autonomous robots and machine learning or data-driven development processes
Experience with ROS, pose estimation, SLAM, probabilistic filtering, 3D data, or CUDA

Nice-to-haves

Bonus Qualifications

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learning

Platform: lever
Effort: quick