Machine Learning Engineer, Simulation Realism

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 foundation models trained on our datasets and diffusion technology. We also invest in capable infrastructure that allows us to quickly set up and roll out multiple scenarios to rigorously test our autonomous driving systems.

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

You will:

• Be part of a world-class applied ML team dedicated to advancing ultra-realistic autonomous vehicle (AV) simulations by leveraging Generative AI technologies.
• Apply deep domain expertise in ML, especially GenAI, to push the boundaries of simulation realism and autonomous driving capabilities.
• Independently manage the entire lifecycle of product innovations, from prototyping to productization, scaling simulations and data processing systems.
• Work within a collaborative applied ML engineering team that transforms research ideas into production-ready solutions.

• 7+ years experience in applied Deep Learning
• 7+ years coding and design skills
• 7+ years of experience solving complex production problems using state-of-the-art ML techniques
• 7+ years of experience taking research to production
• Expertise in Data Analysis or Data Science

Nice to have

• PhD degree in Computer Science or a similar discipline
• Direct experience in Generative AI, including multi-modal foundation models and Diffusion models.