PhD Research Intern, Multi-Modal Foundation Encoder for Perception

Currently working towards a Ph.D., or advanced degree in a relevant engineering program

Good academic standing

Able to commit to a 12-week internship during one of the following summer 2026 cohorts: May 18th - August 7th, OR May 26th - August 14th, OR June 15th - September 4th

At least one previous industry internship, co-op, or project completed in a relevant area

Ability to relocate to the Bay Area, California (or Boston, Massachusetts) for the duration of the internship

Interns at Zoox may not use any proprietary information they are working on as part of their thesis, any published work with their university, or to be distributed to anyone outside of Zoox

Currently enrolled in a Ph.D. program in Computer Science, Electrical/Computer Engineering, Robotics, or a related field with a focus on Deep Learning, Computer Vision, or Autonomous Driving.

Publication record in top-tier AI/Robotics conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICRA).

Prior experience designing and training foundation models (such as World Models, VLMs, LLMs, or VLAs) using large-scale multi-modal autonomous driving datasets.

Hands-on experience developing deep learning models for 3D object detection, tracking, or 3D segmentation.

Experience working with multi-modal sensor data, specifically combining representations from vision, LiDAR, Radar, or language.

Experience with 4D radar object detection or handling sensor data in adverse weather conditions.

Experience with cross-modal alignment for zero-shot detection.

Familiarity with 3D Gaussian Splatting, voxel grid representations, or streaming sparse occupancy prediction.

Experience with self-supervised pre-training or masked sensor modeling.