Senior Staff Engineer, Hardware Architecture, Google Cloud
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Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Physics, a related field, or equivalent practical experience.
- 8 years of experience working in a data center hardware technical environment.
- 5 years of experience in technical leadership.
- Experience in the design of hyperscale servers or data center storage and networking equipment.
- Experience in hyperscale data center networking and ASIC design and implementation.
Preferred qualifications:
- PhD in Electrical Engineering, Computer Engineering, Physics, a related field, or equivalent practical experience.
- 5 years of experience working for a hyperscaler data center company.
- Experience with TCO and ROI analysis of proposed solutions for data centers.
- Experience with data center deployments and their challenges, yield, MTBF, swap rates, quality and reliability improvements, and familiarity with data center infrastructure.
- Knowledge of hardware related to networking, complex ASICs, ML, GPU systems, and infrastructure design and implementation.
About the job
As a Senior Staff Hardware Engineer, you will work on Machine Learning/AI hardware systems projects to craft the solutions for current and future data center deployments. You will work with Product teams to ensure that goals are met with systems and will work with ASIC/FPGA, Software, and Verification teams to ensure proper verification of features. You will also work with the manufacturing teams to ensure that designs are manufacturable and ready for volume production, and with the field teams to support systems that are deployed in the data center.
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.
The US base salary range for this full-time position is $221,000-$311,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Provide technical leadership in ML/AI roadmap discussions and collaborate with outside vendors and internal Engineering teams, focusing on server and CPU architecture, TPU and GPU ASIC, and systems architecture.
- Provide integration expertise, technical analysis, and critical data analysis related to the overall system architecture and performance.
- Provide guidance and direction and work on large data constructs that may be analyzed computationally to reveal patterns, trends, and associations to improve Google’s technical infrastructure.
- Develop and maintain functional relationships across multidisciplinary teams to anticipate future data center architectures and provide key technical interface to key internal partner organizations including software, network, hardware, and commodity management.
- Work closely with the Data Center, ASIC, Platforms, and Google Cloud teams to propose and execute large-scale landings for data center projects.
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