Research Scientist, World Model Post-Training

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.

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.

In this hybrid role, you will report to a Software Engineering Manager.

You will:

• Conduct applied world model research and development
• Post-train Waymo’s world model
• Design compelling experiments by training and evaluating large generative models
• Present results to peers and leadership
• Leverage AI to write high quality code, unit tests, and documentation, and automate workflows

You have:

• Masters or PhD in deep learning in generative / world modeling
• 3+ years of experience with modern deep learning frameworks (e.g., JAX, TensorFlow 2, Pytorch)
• Experience with diffusion / flow matching models
• Experience with reinforcement learning
• Experience training large foundational models

We prefer:

• Experience with post-training of foundational models
• Experience with reinforcement learning for diffusion / flow matching models
• Experience with distillation
• Experience with autonomous vehicles

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. 

Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.