ML Accelerator Architect

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 DriverTM—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 fully 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 closely with many other teammates to ensure we design and optimize hardware and software for maximum performance. We are a multidisciplinary team seeking curious and talented teammates to work on one of the world's highest performance automotive compute platforms.

In this role, you will report to a Hardware Engineering Manager.

You will:

• Analyze workloads and map them efficiently to hardware, proposing novel HW-friendly implementations and projecting performance
• Architect, simulate and design amazing machine learning solutions for our autonomous driving technology
• Work closely with compiler and model developers to influence engineering trade-offs and future model architectures
• Build scalable tools for simulator modeling and performance evaluation
• Interact with cross-functional engineering teams to identify opportunities and requirements

You have:

• BS degree in Computer Science or Computer Engineering or similar relevant technical field, or equivalent practical experience
• 3+ years on designing/architecting complex, high performance architectures - CPUs, GPUs and/or ML accelerators - in the industry or through doctoral research
• 1+ years experience with machine learning architectures, acceleration and model optimization