Research Intern, Scene Understanding and Planning

About the Role

We are seeking a Research Intern to help give our planner a more accurate and up-to-date picture of the world, so the vehicle handles complex real-world scenes more robustly. In this role, you will take on hard, open problems from real-world driving and solve them with frontier research methods. You will report directly to the team lead and work on the problem from definition to validated solution.

Key Responsibilities

• Survey the latest research in scene understanding, prediction, and planning, and turn it into practical approaches that fit our system and constraints.
• Design and implement deep learning models that build a richer, real-time understanding of the driving environment from onboard sensors and other available sources.
• Train and evaluate models on large-scale data from our driving logs and on public benchmarks, and lead failure analysis to find out where and why models fall short.
• Define metrics and evaluation methods that connect perception quality to how the vehicle actually drives.
• Turn successful prototypes into solutions that work with our planning stack, with help from production engineers.
• Collaborate with Perception, Prediction, Planning, and Mapping teams so results move from experiment to product.

Requirements & Tech Stack

• Strong programming skills in Python, and experience with modern deep learning frameworks such as PyTorch or JAX.
• Hands-on experience designing, training, and debugging deep learning models for computer vision, 3D perception, or sequential decision-making.
• Solid understanding of autonomous driving fundamentals, including perception, prediction, and motion planning.
• Ability to read research critically, form hypotheses, and turn them into well-controlled experiments.
• Excellent communication and collaboration skills.

Qualifications

• Currently enrolled in an M.S. or Ph.D. program in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field, for the full duration of the internship.
• Available for a full-time onsite internship of 3–6 months; start dates are flexible.
• Publications at top venues such as CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, ICRA, IROS, CoRL or IV (Preferred).
• Experience with multi-modal sensor fusion (camera, lidar, radar) or BEV representations (Preferred).
• Experience with transformers, graph neural networks, or other structured scene representations (Preferred).
• Experience with uncertainty estimation, or with learning-based prediction or planning (Preferred).
• Experience with large-scale model training and data pipelines (Preferred).
• Experience with public autonomous driving datasets such as nuScenes, Argoverse 2, or the Waymo Open Dataset (Preferred).
• Proficiency in C++ (CUDA is a plus) and experience with robotics systems (Preferred).