Minimum qualifications:
- Bachelor's degree in Electronics or Computer Science Engineering, or equivalent practical experience
- 8 years of experience working with System Software
- Experience of Silicon architecture and memory subsystem
- Experience in Power and Performance optimization
- Master's degree in Electronics Engineering, Computer Engineering, or Computer Science
- Experience in Power and Performance Optimization
- Experience in mobile SoC architecture and system software architecture
- Experience with system hardware/software design for power, performance, and thermal
- Knowledge of AI/ML/LLM on the edge device
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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.
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
- Understand the Silicon hardware and software architecture, design, and implementation of a SoC.
- Understand software workloads and utilize the available compute engines on the Silicon.
- Define and validate key performance metrics (KPIs) and power goals for AI/ML/LLM workloads.
- Engage with Machine Learning System Architects and Software teams to define and validate hardware/software specifications.