Machine Learning Solutions Architect, Hardware

About the Role

Waymo's Compute Team delivers the compute platform responsible for running the fully autonomous vehicle's software stack. This involves architecting and creating high-performance custom silicon, developing system-level compute architectures, and collaborating with other teams to optimize hardware and software for maximum performance. This is a multidisciplinary team focused on one of the world's highest performance automotive compute platforms.

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

Responsibilities

• Analyze workloads and map them efficiently to hardware, proposing novel HW-friendly implementations and projecting performance.
• Architect, simulate, and design machine learning solutions for 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.

Requirements

• 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.
• Strong C++ programming and algorithmic problem solving skills.

Preferred Qualifications

• 1+ years modeling high performance architectures in cycle-aware simulators.
• Track record of analyzing workloads and architecting, delivering novel HW+SW solutions to vastly improve performance, efficiency.
• Familiarity with ML model architectures and their compute characteristics (bottlenecks, optimization opportunities).
• Experience with microarchitecture design (SystemVerilog or HLS).