Senior ML Compiler Engineer, Compute

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.

Waymo's Compute Team is tasked with a critical and exciting mission: We deliver the compute platform responsible for running the autonomous vehicle's software stack. To achieve our mission, we architect and create high-performance custom silicon; we develop system-level compute architectures that push the boundaries of performance, power, and latency; and we collaborate with many other teammates to ensure we design and improve hardware and software for maximum performance. We are a diverse team looking for curious and talented teammates to work on one of the world's highest performance automotive compute platforms.

This role follows a hybrid work schedule and you will report to a Software Engineering Manager.

This position will require the ability to work some hours that align with the team in the Pacific Time zone on an as needed basis.

You will:

• Analyze the performance characteristics of code generated by our production grade compiler, and design and implement optimizations to improve that performance


• Design and implement compiler support for novel features of our high-performance architecture


• Work with hardware architects to understand and influence the development of our unique neural network inference platform through hardware/software codesign


• Work with model developers to tune their neural networks for better inference efficiency and accuracy

You have:

• BS degree in Computer Science/Electrical Engineering or equivalent experience and 5+ years of industry experience OR


• MS degree in Computer Science/Electrical Engineering and 3+ years of industry experience


• PhD degree in Computer Science/Electrical Engineering and 1+ years of industry experience


• 3+ years experience working on compilers for parallel architectures


• 1+ years experience working with ML inference or linear algebra computation


• C++ programming skills

We prefer:

• Python programming experience


• Experience with compilers for neural networks


• Knowledge of computer architectures used for neural network inference, and neural network performance characteristics


• Knowledge of the principles behind popular machine learning and neural network algorithms and applications