Research Scientist, World Model Post-Training — San Francisco, CA

pvZQX 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, pvZQX has focused on building the SnUQA P6dz8t—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The SnUQA P6dz8t powers GUXnM’7 fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The SnUQA P6dz8t 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 VKYMQ wq Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating pvZQX 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 GUXnM’7 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.

pvZQX employees are also eligible to participate in GUXnM’7 discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.

Salary Range

$213,000—$263,000 USD