CPU Workload Analysis Researcher, PhD Graduate, Google Cloud

Minimum Qualifications:

• PhD in Electrical and Electronics Engineering, or equivalent practical experience.
• 2 years of experience with software development in C++ programming language.
• 1 years of experience with data structures or algorithms.

Preferred Qualifications:

• Experience in performance modeling, performance analysis, and workload characterization.
• Experience applying machine learning techniques and inference usage models on hardware.
• Expertise in CPU architecture disciplines such as branch prediction, prefetching, value prediction, and caching policies.

About The Job

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.

As a CPU Workload Analysis Researcher within Google Cloud's MSCA organization, you will be integral to developing silicon solutions powering Google's direct-to-consumer products. You will join a Research and Development team focused on analyzing and profiling workloads requirements within the Google Cloud environment. Your role will involve conducting in-depth research on CPU optimization, feature development, and ML usages over compute platforms, contributing to identifying key areas of investment and future opportunities. This role offers a unique opportunity to perform groundbreaking research with a significant impact on both research methodologies and industry products, within the server chip architecture team. Your work will directly influence the next generation of hardware experiences for millions of Google users and Cloud customers.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.

Responsibilities

• Plan and execute detailed analysis of CPU workloads within the Google Cloud infrastructure, analyze trends and map future requirements.
• Collaborate closely with architecture and modeling owners to understand design specifications and identify critical scenarios related to CPU performance and efficiency.
• Develop and implement custom workload generation tools and methodologies to simulate real-world usage patterns on Google Cloud platforms.
• Analyze the impact of machine learning applications on CPU usage, identifying opportunities for optimization and feature enhancements.
• Lead the investigation and development of metrics to measure CPU performance and efficiency, presenting findings to stakeholders and contributing to strategic decisions.