Silicon RTL Design Engineer, PhD, Google Cloud

In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive TPU (Tensor Processing Unit) technology that powers Google's most demanding AI/ML applications. You’ll be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's TPU. You'll contribute to the innovation behind products loved by millions worldwide, and leverage your design and verification expertise to verify complex digital designs, with a specific focus on TPU architecture and its integration within AI/ML-driven systems.

In this role, you will shape the future of AI/ML hardware acceleration as a silicon architect/design engineer and drive Tensor Processing Unit (TPU) technology that fuels Google's most demanding AI/ML applications. You will collaborate with hardware and software architects and designers to architect, model, analyze, define and design next-generation TPUs. You will have dynamic, multi-faceted responsibilities in areas such as product definition, design, and implementation, collaborating with the engineering teams to drive the optimal balance between performance, power, features, schedule, and cost.

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 team behind Google's groundbreaking innovations, empowering the development of our 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.

• Revolutionize Machine Learning (ML) workload characterization and benchmarking, and propose capabilities and optimizations for next-generation TPUs.
• Develop architecture specifications that meet current and future computing requirements for AI/ML roadmap. Develop architectural and microarchitectural power/performance models, microarchitecture and RTL designs and evaluate quantitative and qualitative performance and power analysis.
• Partner with hardware design, software, compiler, Machine Learning (ML) model and research teams for effective hardware/software codesign, creating high performance hardware/software interfaces.
• Develop and adopt advanced AI/ML capabilities, drive accelerated and efficient design verification strategies and implementations.
• Use AI techniques for faster and optimal physical design convergence-timing, floor planning, power grid and clock tree design, etc. Investigate, validate, and optimize DFT, post-silicon test, and debug strategies, contributing to the advancement of silicon bring-up and qualification processes.

Minimum qualifications:

• PhD degree in Electronics and Communication Engineering, Electrical Engineering, Computer Engineering, a related technical field, or equivalent practical experience
• Experience in programming languages (e.g., C++, Python, Verilog), Synopsys, Cadence tools.
• Experience with accelerator architectures and data center workloads.

Preferred qualifications:

• 2 years of experience in Silicon engineering post PhD.
• Experience with performance modeling tools.
• Knowledge of arithmetic units, bus architectures, accelerators, or memory hierarchies.
• Knowledge of high-performance and low-power design techniques.

• PhD degree in Electronics and Communication Engineering, Electrical Engineering, Computer Engineering, a related technical field, or equivalent practical experience
• Experience in programming languages (e.g., C++, Python, Verilog), Synopsys, Cadence tools.
• Experience with accelerator architectures and data center workloads.
• 2 years of experience in Silicon engineering post PhD.
• Experience with performance modeling tools.
• Knowledge of arithmetic units, bus architectures, accelerators, or memory hierarchies.
• Knowledge of high-performance and low-power design techniques.