The mandate of the learned behavior team is to use advanced machine learning techniques to accelerate software progress. In this role, you will work closely with the software vertical teams to understand their pain points and explore novel and advanced machine learning methods to solve practical real-world challenging problems. To name a few, using self-supervised learning to learn robust representations, exploring techniques for out-of-distribution detection to solve long tail problems, adjusting reinforcement learning techniques for motion planning, working on trajectory prediction and motion planning, investigating the robustness of models to mitigate uncertainties, or trying to build an end-to-end driving system. If you love solving challenging new problems with a mindset of deriving practical solutions to eventually be used on the vehicle, come join us!

Work on scalable machine learning based planning and prediction systems to generate safe and feasible trajectories for autonomous driving.

Research generative sequence modeling and sequential decision making. Research backgrounds we are looking for but not limited to are: Nice to have: Embodied AI for robotics, Causal reasoning, Model interpretability and explainability, Joint prediction and planning.

Mitigate uncertainties accumulated through interconnected autonomy components.

Collaborate across autonomy teams while developing holistic solutions to top autonomy challenges. Understand issues, propose ideas, prioritize work and develop solutions to solve them.

Derive practical solutions and deploy them on real-world vehicles.