Job description

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 Simulator Team at Waymo builds state-of-the-art simulations of realistic environments for testing and training the Waymo Driver. Our team is a diverse and collaborative group of software engineers, machine learning (ML) engineers, and data scientists. We develop industry-leading simulation solutions using advanced ML algorithms that measure and enhance the performance of the Waymo Driver. By applying machine learning, we model the real world, including realistic agents (vehicles, pedestrians, cyclists, motorcyclists), roads, traffic control systems, and weather conditions. To increase the fidelity and steerability of our simulations, we employ the latest ML technologies such as large foundational World Model trained on our datasets, world understanding and reasoning capabilities for tail cases, and reinforcement learning for long term credit assignment. We also invest in capable infrastructure that allows us to quickly set up and roll out multiple counterfactual scenarios to rigorously test our autonomous driving systems. In this hybrid role, you will report to a Staff Technical Lead You will: Drive innovation in ultra-realistic world simulation for autonomous vehicles by developing and scaling foundation models for embodied agents. Design and train multimodal world models and generative architectures using diverse sensory data (such as RGB images, video, and text). Drive closed-loop training and reinforcement learning pipelines to advance simulation realism, policy optimization, and decision-making for autonomous driving. Work with Waymo's foundational AI research team to transfer research into scalable, production-ready solutions. You have: 5+ years of experience with ML software engineering 5+ years of experience with Python 5+ years of ML software experience with machine learning frameworks including PyTorch, TensorFlow, or JAX/Flax Demonstrated experience building, training, or scaling world models or multi-modal models on large-scale datasets (e.g., video, images, text) Hands-on experience with closed-loop training, policy learning, or reinforcement learning frameworks We prefer: Experience in robotics, control theory, or embodied AI (strongly preferred, but not required) Experience working with, creating, and/or developing ML models for autonomous driving systems Strong C++ in a production setting, with experience in integrating ML models into production systems Experience in training large-scale models on GPU/TPU clusters 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. Salary Range $213,000 — $263,000 USD

Requirements

Must-haves

In this hybrid role, you will report to a Staff Technical Lead
Drive innovation in ultra-realistic world simulation for autonomous vehicles by developing and scaling foundation models for embodied agents.
Design and train multimodal world models and generative architectures using diverse sensory data (such as RGB images, video, and text).
Drive closed-loop training and reinforcement learning pipelines to advance simulation realism, policy optimization, and decision-making for autonomous driving.
Work with Waymo's foundational AI research team to transfer research into scalable, production-ready solutions.
5+ years of experience with ML software engineering
5+ years of experience with Python
5+ years of ML software experience with machine learning frameworks including PyTorch, TensorFlow, or JAX/Flax
Demonstrated experience building, training, or scaling world models or multi-modal models on large-scale datasets (e.g., video, images, text)
Hands-on experience with closed-loop training, policy learning, or reinforcement learning frameworks
Experience working with, creating, and/or developing ML models for autonomous driving systems
Strong C++ in a production setting, with experience in integrating ML models into production systems
Experience in training large-scale models on GPU/TPU clusters

Nice-to-haves

Experience in robotics, control theory, or embodied AI (strongly preferred, but not required)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlaipython

Platform: greenhouse
Effort: quick