PhD Research Intern — Learned End-to-End Driving and Actuation

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Description

Most autonomy stacks are built around a single major sensor, usually the camera, with every other sensor secondary. Zendar took a different path: an early-fusion camera and radar perception system, in bird's-eye view and in 3D, in which each sensor plays a major role, trained data-efficiently on a multi-million-sample labelled dataset we collected ourselves. We own the whole pipeline, sensor setup and calibration, learned perception, lane estimation and map integration, and that vertical integration lets us move fast on hard problems. We are now turning to what comes after perception: action and actuation, using the full pipeline to actuate autonomously in the real world, for autonomous vehicles and robotics, in a learned, end-to-end way. A learned motion planner drives on that stack today; this role is about the research that takes it further. The Perception and Machine Learning Team is looking for a final-year PhD student to join us as a Research Intern on learned planning and actuation, on-site in Berkeley, CA. This internship offers an opportunity for both the intern and the team to assess mutual fit for a potential full-time research position. The Role You will own one research direction on the production planner, working with the researchers who ship it, on real fleet data, and present your results to the people who decide what ships. What a successful planner means to us, and what we will measure you against: Understands what is missing: finds the mechanism behind a dr

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