Machine Learning Engineer

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 Senior Staff Tech Lead Manager

You will:

• Drive innovation in ultra-realistic world simulation for autonomous vehicles. You will develop the latest simulation technologies using foundation models for embodied agents.
• Apply your deep Machine Learning (ML) expertise, in Generative AI (GenAI), to push the boundaries of simulation realism and directly influence the advancement of autonomous driving technology.
• Work with Waymo's foundational AI research team to transfer research into scalable and 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 framework including Pytorch, Tensorflow, Jax/Flax ,

We prefer:

• Experience working with, creating and/or developing ML models for robotics and/or self driving vehicles
• Strong C++ in production setting, with experience in integrating ML models in production system
• Experience in training large scale models on GPU/TPU clusters are strongly preferred

#LI-Hybrid