Machine Learning Engineer

About The Company
Waymo is a pioneering autonomous driving technology company dedicated to creating the most trusted driver in the world. Originating from the Google Self-Driving Car Project established in 2009, Waymo has consistently focused on advancing autonomous vehicle capabilities to enhance mobility access and improve safety on roads. The company’s flagship innovation, the Waymo Driver, is recognized as the world's most experienced autonomous driver, having autonomously driven over 100 million miles on public roads and completed over ten million rider-only trips across more than 15 states in the U.S. Through its cutting-edge technology, Waymo aims to reduce traffic-related fatalities and revolutionize transportation, making it safer, more accessible, and more efficient.
About The Role
We are seeking a talented ML Infrastructure Engineer to join our team, focusing on advancing state-of-the-art ultra-realistic multi-agent simulations utilizing foundation models. In this role, you will operate at the intersection of machine learning infrastructure, simulation engineering, and foundation models. Your primary responsibilities will involve designing, building, and optimizing high-performance simulation environments and business logic that run directly on TPU hardware using frameworks such as JAX and TensorFlow. You will play a crucial role in developing scalable model and data parallelism strategies to support large-scale training and inference of foundation models, which are integral to our autonomous driving systems. Collaboration with modeling teams will be essential to integrate foundation models seamlessly into simulation pipelines, ensuring the delivery of realistic and efficient environments for reinforcement learning and other AI applications.
Qualifications
6+ years of professional software engineering experience, with at least 4 years dedicated to machine learning infrastructure development.
Proficiency in ML programming on TPU and GPU hardware using frameworks such as JAX, PyTorch, or TensorFlow.
Hands-on experience in scaling large models through model parallelism, data parallelism, or distributed training techniques.
Strong understanding of modern ML models, including autoregressive transformers, and proficiency with ML accelerator profiling tools.
Ability to independently lead ambiguous technical initiatives from conception to deployment, building robust libraries, pipelines, and developer tools.
Excellent verbal and written communication skills for effective collaboration across cross-functional and distributed teams.
Responsibilities
Design, develop, and optimize realistic simulation environments and business logic running on TPUs using JAX and TensorFlow.
Implement and refine large-scale model and data parallelism strategies to facilitate efficient training and inference on TPU hardware.
Collaborate with modeling teams to integrate foundation models into simulation workflows, ensuring consistency and performance.
Profile system performance, identify bottlenecks across ML accelerators, and optimize end-to-end execution speed.
Translate product and business goals into detailed technical requirements and deliverables.
Drive system architecture decisions, from data engineering to simulation execution, to meet performance and scalability targets.
Benefits
Competitive salary within the range of $251,000 to $310,000 USD, commensurate with experience and location.
Participation in Waymo’s discretionary annual bonus program and equity incentive plan.
Comprehensive health, dental, and vision insurance coverage.
Generous paid time off and flexible work arrangements.
Access to ongoing professional development and training opportunities.
Participation in a mission-driven company committed to safety, innovation, and societal impact.
Equal Opportunity
Waymo is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity or expression, age, disability, or any other legally protected status. All employment decisions are made based on qualifications, merit, and business needs.

Work arrangement
Yes