About the job
In this role, you’ll work to shape the future of AI/ML hardware acceleration. You will have an opportunity to drive cutting-edge 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.
Responsibilities
Architect and implement next generation physical design EDA, and CAD tool workflows for ASIC development.
Collaborate with chip design teams to implement tools and methodologies for physical design in leading edge process nodes.
Develop auditing tools, checkers, and metric dashboards based on APIs from third-party EDA tools.
Own the physical design of blocks and subsystems end-to-end.
Qualifications
Minimum
Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
8 years of experience with physical design flow and methodologies.
Experience with EDA tools for physical design (e.g., Cadence, Synopsys, Siemens).
Experience in full-chip or block-level physical design.
Experience with scripting in Python, Tcl, or Perl.
Preferred
Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
10 years of experience in physical design flow and methodologies for high-performance ASIC/SoC projects.
Experience in sign-off areas such as physical verification (Caliber/IC Validator), Formal Verification (LEC), extraction, low power verification, STA closure, and ECO flows.
Experience in achieving optimal Power, Performance, Area (PPA) goals in complex designs.
Familiarity with 2.5D/3D IC packaging and proficiency with advanced parasitic extraction tools (e.g., STARRC).
Ability to develop and deploy repeatable design methodologies, focusing on low-power verification.