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
