Zoox’s internship program provides hands-on experiences with state of the art technology, mentorship from some of the industry's brightest minds, and the opportunity to play a part in our success. Internships at Zoox are reserved for those who demonstrate outstanding academic performance, activities outside their course work, aptitude, curiosity, and a passion for Zoox's mission.

The Autonomy Behavior group at Zoox develops the planning and control systems that enable the vehicle to make safe, comfortable, and efficient driving decisions. These engineers work on planner capabilities, control systems, behavior compute and integration, onboard behavior model architectures, and data optimization to ensure our robotaxi drives safely and naturally in complex urban environments.

As an MLE Intern working on Autonomy Behavior, you may be assigned to one of the following teams:

On the Hydra team, you will explore ways to improve introspection for the ML planner as well as improve model performance. This team is developing a hybrid planning system that combines a learned planner component with a search based supervisor system. In this role, you will be exploring improving the introspectability of the learned planner as well improving the performance through experiments. You will have the opportunity to learn from world class experts on Imitation Learning, Reinforcement Learning and potential to demonstrate your work both in simulation and on our robots.

On the Onboard Behavior Encoder team, you will be responsible for modeling and scaling ML architectures that enable behavior decision processes in autonomous vehicles. In this role, you'll explore novel approaches to behavior modeling, including research at the intersection of LLM models and motion planning systems. You'll drive performance improvements through rigorous experimentation and have the opportunity to work alongside world-class ML researchers and autonomous driving experts. Your contributions will directly influence how our vehicles navigate complex real-world scenarios.

On the Prediction Integration team, you will be a key contributor to the autonomous vehicle's real-world driving behavior by improving the performance of the ML Planner. This role offers the chance to develop innovative methods for analyzing and explaining the ML Planner's decisions. You will also benefit from learning directly from leading experts in autonomous driving and ML research.