Machine Learning Engineer, AI Foundations

@ Waymo

About Waymo

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 One, a fully autonomous ride-hailing service, and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over one million rider-only trips, enabled by its experience autonomously driving tens of millions of miles on public roads and tens of billions in simulation across 13+ U.S. states.

The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo 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.

This role can be based out of the Oxford or London, UK office and the work modality is hybrid. In this role you will report to a Sr. Staff Machine Learning Engineer.

Your Responsibilities

• Work with a creative team to design and evaluate large-scale ML models.
• Frame real-world problems as well-defined ML problems and develop solutions.
• Collaborate with teams, including ML infrastructure and research groups.
• Develop cutting-edge ML approaches like deep learning and reinforcement learning.
• Scale solutions to Google-sized data pipelines for real-time applications.

Qualifications

You have:

• Good programming skills - Python, JAX, TensorFlow/PyTorch.
• Strong statistical / ML knowledge.

We prefer:

• C++ experience.
• ML infra experience: training and evaluating ML models at scale.
• Deep learning experience, especially with generative models.
• Autonomous driving experience.