About the job

Our computational challenges are so big, complex and unique we can't just purchase off-the-shelf hardware, we've got to make it ourselves. Your team designs and builds the hardware, software and networking technologies that power all of Google's services. As a Hardware Engineer, you design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You develop from the lowest levels of circuit design to large system design and see those systems all the way through to high volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers affecting millions of Google users.

With your technical expertise, you lead projects in multiple areas of expertise (i.e., engineering domains or systems) within a data center facility, including construction and equipment installation/troubleshooting/debugging with vendors.

As an Executive System Software and Performance Architect, you will collaborate with talents in Android, Pixel, AI, and CPU teams to develop cutting-edge CPU technologies. You will explore workloads and CPU design together to provide unique, engaging and actionable insights for hardware and software co-design. You will build traces and benchmarks to provide a consistent view of the workload for all stakeholders to do performance evaluation. You will contribute to enhancing many Google’s products and to the success of Google’s CPU project.

Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.

Responsibilities

• Lead the development of methodologies, tools, and infrastructures to enable effective hardware, software co-design.
  
• Drive cross-team collaboration to design, implement and enhance performance analysis and trace collection infrastructures.
  
• Characterize critical workloads and identify performance bottlenecks or patterns. 
  
• Create representative instruction traces and benchmarks for performance and microarchitecture explorations.
  
• Propose CPU-focused Android and AI workload optimization solutions.