Design Technology Co-Optimization Engineer

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.

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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $198000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Execute high-fidelity Place and Route (P&R) experiments to evaluate the PPA impact of advanced process features, library architectures, and design rule variations on datacenter-class IP.
• Drive Design Technology Co-Optimization (DTCO) by collaborating with foundries and internal technology teams to define optimal metal stacks, track heights, and scaling boosters (e.g., backside power delivery, buried power rails).
  
• Quantify process entitlement through systematic benchmarking of logic and memory macros, identifying bottlenecks in power density and timing closure for next-generation nodes.
  
• Develop automated physical design methodologies and flows to accelerate technology pathfinding and enable rapid what-if analysis of emerging transistor architectures.
  
• Influence System Technology Co-Optimization (STCO) by partnering with Hardware Architects and Circuit Designers to translate process-level innovations into system-level performance gains.
  

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
  
• 2 years of experience in Physical Design (RTL-to-GDS) or Technology Development, focusing on advanced nodes (e.g., 7nm, 5nm, or below).
• Experience in scripting and automation using Tcl and Python (or Perl) to manage design sweeps and data extraction.
  
• Experience with industry-standard Place and Route (P&R) tools and Static Timing Analysis (STA) tools.
• Experience in CMOS device physics, FinFET/nanosheet architectures, and the impact of layout parasitics on PPA.
  

Preferred qualifications:

• Master's degree or PhD in Electrical Engineering, Computer Engineering, or Computer Science, with an emphasis on computer architecture.
• Experience working with major foundry technology files (PDKs) and interpreting Design Rule Manuals (DRM) to guide physical implementation.
• Experience in Design Technology Co-Optimization (DTCO), including standard cell library characterization, metal stack optimization, and evaluation of scaling boosters (e.g., backside power delivery).
• Experience with RTL synthesis and standard cell library optimization.
  
• Expertise in power integrity and reliability analysis and physical verification.
  
• Familiarity with datacenter-class IP blocks, such as high-performance CPU/GPU cores, SRAM arrays, or high-speed interconnects.