Senior/Staff Software Engineer - Mission Progress

On the Motion Planning team at Zoox, you'll have the unique opportunity to tackle the cutting-edge problem of improving the robot's driving behaviors on public roads. Specifically, within the Mission Progress team, you will focus on one of the most open-ended problems: pickup and dropoff. Pickup/dropoff can range from simple maneuvers on quiet suburban streets to incredibly complex and ambiguous driving scenarios that even humans have difficulty navigating, like airports and unstructured hotel entrances.
Getting pickup/dropoff maneuvers to be safe and reliable is not only a technical challenge but also a way for robotaxi services to eventually distinguish themselves in their user experience. Pickup/dropoff is the first and last thing a rider experiences and has the potential to leave lasting impressions on the rider. This means you will work closely with UI/UX and product to engineer solutions that are not just safe but elegant in how they handle the different human considerations in the pickup/dropoff process.
In this role, you will: Design principled algorithmic improvements across different driving behavior categories and implement those changes in our code base.Use our extensive backend tooling to test your changes across many different driving scenarios 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 B.S or M.S. degree in Computer Science, Mechanical Engineering, or related field and 8+ years of experienceFluency in C++Understanding of configuration spaces and a variety of planning techniques (A*, RRTs, PRMs, etc.)Demonstrated ability to create real-time motion planning algorithmsBonus Qualifications Experience working with planning problems related to pickup/dropoff or pullover maneuversExperience with machine learning applications in motion planningSignificant contributions to geometric- and/or sampling-based planning algorithms

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