Job description

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. In this role, you will be empowering Google and our customers with breakthrough capabilities by delivering transformational, co-optimized silicon and technology leadership at scale. 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 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. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits Learn more about benefits at Google . • Own critical custom datapath and matrix multiplication accumulator blocks, driving execution from RTL-to-GDSII including synthesis, floor-planning, place and route (PNR), timing closure, and physical signoff. • Understand math-intensive micro-architecture to perform detailed feasibility studies, analyzing performance, power, and area (PPA) tradeoffs to achieve optimal design closure. • Develop and improve advanced physical design methodologies and customize implementation recipes, specifically optimizing PPA across complex arithmetic pipeline structures. Manage different PNR tools - synopsys fusion compiler, cadence (Innovus/Genus), PrimeTime, StarRC, Calibre, Apache Redhawk. • Implement and execute key design phases: floor-planning, synthesis, placement, clock tree synthesis (CTS), timing closure, routing, extraction, physical verification (design rule checking (DRC)/layout versus schematic (LVS), electromigration (EM)/IR, and final signoff. • Lead sub-chip execution and drive delivery, providing direct technical guidance and mentorship to a small team of engineers to ensure on-schedule project completion. • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience. • 10 years of experience in physical design, including custom structured datapath implementation. • Experience hardening dense compute units (such as dot-product engines, multiplier-accumulator (MACs), multipliers, or arithmetic logic unit (ALUs) into high-frequency, low-power macros. • Experience in sub-7nm process nodes (including FinFET and Gate-All-Around architectures), managing the physical density and routing congestion typical of custom datapaths. Minimum qualifications: • Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience. • 10 years of experience in physical design, including custom structured datapath implementation. • Experience hardening dense compute units (such as dot-product engines, multiplier-accumulator (MACs), multipliers, or arithmetic logic unit (ALUs) into high-frequency, low-power macros. • Experience in sub-7nm process nodes (including FinFET and Gate-All-Around architectures), managing the physical density and routing congestion typical of custom datapaths. Preferred qualifications: • Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with focus on computer architecture. • Experience in structured placement methodologies (e.g., relative placement, data-flow driven layout, and customized power-grid stitching for dense blocks). • Experience with tools for logic synthesis, place and route, timing analysis, and design checks for physical and electrical quality; familiarity with tools for schematics, layout, and circuit/logic simulation. • Experience with physical design and timing, with managing PT-to-PnR timing correlations and metal-fill impacts on dense structures. • Expert-level scripting versatility (Tcl, Python, Perl) to build layout automation and structural constraints within standard EDA tools.

Requirements

Must-haves

In this role, you will be empowering Google and our customers with breakthrough capabilities by delivering transformational, co-optimized silicon and technology leadership at scale.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Own critical custom datapath and matrix multiplication accumulator blocks, driving execution from RTL-to-GDSII including synthesis, floor-planning, place and route (PNR), timing closure, and physical signoff.
Understand math-intensive micro-architecture to perform detailed feasibility studies, analyzing performance, power, and area (PPA) tradeoffs to achieve optimal design closure.
Lead sub-chip execution and drive delivery, providing direct technical guidance and mentorship to a small team of engineers to ensure on-schedule project completion.
Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
10 years of experience in physical design, including custom structured datapath implementation.
Experience hardening dense compute units (such as dot-product engines, multiplier-accumulator (MACs), multipliers, or arithmetic logic unit (ALUs) into high-frequency, low-power macros.
Experience in sub-7nm process nodes (including FinFET and Gate-All-Around architectures), managing the physical density and routing congestion typical of custom datapaths.
Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
10 years of experience in physical design, including custom structured datapath implementation.
Experience hardening dense compute units (such as dot-product engines, multiplier-accumulator (MACs), multipliers, or arithmetic logic unit (ALUs) into high-frequency, low-power macros.
Experience in sub-7nm process nodes (including FinFET and Gate-All-Around architectures), managing the physical density and routing congestion typical of custom datapaths.

Nice-to-haves

US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits
Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with focus on computer architecture.
Experience in structured placement methodologies (e.g., relative placement, data-flow driven layout, and customized power-grid stitching for dense blocks).
Experience with tools for logic synthesis, place and route, timing analysis, and design checks for physical and electrical quality; familiarity with tools for schematics, layout, and circuit/logic simulation.
Experience with physical design and timing, with managing PT-to-PnR timing correlations and metal-fill impacts on dense structures.
Expert-level scripting versatility (Tcl, Python, Perl) to build layout automation and structural constraints within standard EDA tools.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpython

Platform: google
Effort: medium