Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. The Autonomy Behavior group at Zoox develops the planning and control systems that enable the vehicle to make safe, comfortable, and efficient driving decisions in complex urban environments.
Requirements
- Advanced understanding of Python or C++
- Strong experience with PyTorch and/or JAX
- Experience with production ML pipelines: dataset creation, labeling, training, metrics
- Experience with Neural Network design and implementation
- Strong track record in machine learning for autonomous driving, robotics, or LLMs
- Familiarity with applying ML visualization and introspection techniques
Responsibilities
- explore ways to improve introspection for the ML planner as well as improve model performance
- developing a hybrid planning system that combines a learned planner component with a search based supervisor system
- exploring improving the introspectability of the learned planner based on experiments
- modeling and scaling ML architectures that enable behavior decision processes in autonomous vehicles
- explore novel approaches to behavior modeling, including research at the intersection of LLM models and motion planning systems
- drive performance improvements through rigorous experimentation
- improving the performance of the ML Planner
Other
- Currently working towards a B.S., M.S., Ph.D., or advanced degree in a relevant engineering program
- Must be returning to school to continue your education upon completing this internship
- Good academic standing
- Able to commit to a 12-week internship beginning in May or June of 2026.
- At least one previous industry internship, co-op, or project completed in a relevant area
