Senior Research Scientist, World Modeling

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.

This role follows a hybrid work schedule and reports to a Staff Research Scientist / Tech Lead Manager.

You will:

• Design and implement generative world action modeling solutions to simulate future world states / observations and generate policies for embodied agents (autonomous vehicles).
• Develop and maintain scalable data pipelines to process data from multiple sources.
• Design and implement evaluation strategies for world action models.
• Study and analyze different behaviors of this model, such as scaling efficacy, downstream quality implications, model architecture design ablations, etc.
• Conducting cutting-edge research and potentially communicating research findings to the wider academic community via technical blog posts, technical reports and/or publications.
• Collaborate inclusively across Waymo/Alphabet to apply developed techniques to Waymo products.

You have:

• PhD degree in Computer Science or a similar discipline, or an equivalent amount of deep learning research experience, with 5+ years of experience with Deep Learning and Generative Models.
• Strong coding skills in Python and strong familiarity with major ML Frameworks (JAX, Tensorflow, Pytorch).
• Strong familiarity with AI tools in day to day work. 
• Experience in large-scale distributed training and different forms of parallelism.
• Experience in generative models for domains such as world models, images, videaos, 3D, human animation, traffic, and/or simulation, using techniques such as diffusion and/or autoregressive models.

We prefer:

• Publications at top-tier conferences like CVPR/ICCV/ECCV/ICLR/ICML/NeurIPS/IROS/CoRL etc.
• Substantial involvement in and contributions to high impact industry AI projects. 
• First-hand experience in, and direct contributions to training large world models / world-action models.
• Familiarity with Reinforcement Learning in simulation environments.