2027 Summer Intern, PhD, Research Scientist, Visual Reasoning

Waymo•Mountain View, CA

4d•Hybrid

About The Position

Waymo is seeking a PhD intern for its AI Foundations team, which develops machine learning solutions for open problems in autonomous driving and collaborates with other research teams in Alphabet. The team focuses on areas including reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. Interns partner with industry leaders on impactful projects while applying their knowledge and developing their skills. In this role, you will research and develop visual and spatio-temporal reasoning methods for multimodal Perception Foundation Models using autonomous-vehicle sensor data. You will design and implement post-training approaches, including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), to improve perception generalization in long-tail autonomous-driving scenarios. You will also run large-scale experiments and ablation studies on autonomous-driving benchmarks, with the opportunity to publish findings at leading AI and computer-vision conferences.

Requirements

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

Nice To Haves

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

Responsibilities

Research and develop novel visual and spatio-temporal reasoning methods for multimodal Perception Foundation Models using autonomous-vehicle 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.
Have the opportunity to publish findings at top-tier AI and computer-vision conferences, such as CVPR, ICCV, ECCV, NeurIPS, ICLR, and ICML.