2027 Summer Intern, PhD, Research Scientist, Visual Reasoning

The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.

Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

You will:

• Research and develop novel visual and spatio-temporal reasoning methods for multimodal Perception Foundation Models using AV sensor data
• Design and implement post-training recipes—including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL)—to improve perception generalization in long-tail autonomous driving scenarios
• Conduct large-scale experiments and ablation studies on autonomous driving benchmarks, with the opportunity to publish findings at top-tier AI/CV conferences (e.g., CVPR/ICCV/ECCV/NeurIPS/ICLR/ICML)

• Currently pursuing a PhD in Computer Science, Machine Learning, Robotics, Electrical Engineering or a related technical field
• Hands-on experience & strong publications in perception, and/or LLM/VLM
• Knowledge of pre-training and post-training of the large foundation models
• Strong Python coding skills with expertise in modern deep learning frameworks (e.g., PyTorch, JAX, TensorFlow)

Nice to have

• Experience with visual reasoning for LLM/VLM/robotics/driving
• Experience with RL training in LLM/VLM/robotics
• Experience with open-vocabulary perception