2027 Summer Intern, PhD, Research Scientist, Visual Reasoning
About The Position
Waymo’s AI Foundations team develops machine learning solutions to address open problems in autonomous driving, with the goal of safely operating Waymo vehicles in many cities and under all driving conditions. The team also initiates and fosters collaborations with other research teams in Alphabet. Current areas of focus include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation. As a summer intern, you will research and develop novel visual and spatio-temporal reasoning methods for multimodal Perception Foundation Models using autonomous-vehicle sensor data. You will 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. You will conduct large-scale experiments and ablation studies on autonomous driving benchmarks, with the opportunity to publish findings at leading AI and computer vision conferences. Waymo interns work on impactful projects with industry leaders while applying and developing their skills.
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 LLMs, VLMs, robotics, or driving.
- Experience with RL training in LLMs, VLMs, or robotics.
- 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.
- Potentially publish findings at top-tier AI and computer vision conferences, including CVPR, ICCV, ECCV, NeurIPS, ICLR, and ICML.
Benefits
- Eligible to participate in the company’s benefits programs, subject to eligibility requirements.
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What This Job Offers
Job Type
Full-time
Career Level
Intern
Education Level
Ph.D. or professional degree
