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Home›Jobs›Google›SoC Silicon Top-Level Floorplan Engineer
Google

About Google

Empowering the world through technology and information

🏢 Tech👥 100K+📅 Founded 1998📍 Mountain View, California, United States

Key Highlights

  • Over 100,000 employees globally
  • Headquartered in Mountain View, California
  • Parent company Alphabet Inc. valued at $1.5 trillion
  • Google Cloud Platform serves millions of customers

Google LLC, headquartered in Mountain View, California, is a global leader in internet-related services and products, including its flagship search engine, Google Search, and the Android operating system. With over 100,000 employees, Google also offers cloud computing services through Google Cloud P...

🎁 Benefits

Google offers competitive salaries, equity options, generous PTO policies, comprehensive health benefits, and a remote work policy that allows flexibi...

🌟 Culture

Google is known for its engineering-first culture, emphasizing innovation and collaboration. The company fosters a unique environment that encourages ...

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Google

SoC Silicon Top-Level Floorplan Engineer

Google • Sunnyvale, CA, USA

Posted 2w ago🏛️ On-SiteSeniorHardware engineer📍 Sunnyvale
Apply Now →

Skills & Technologies

PythonTclPerlEda tools3d ic designPhysical designFloorplanningChip assemblyTsv planningPpa optimization

Job Description

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 (e.g., with a focus on floorplanning, integration, or top-level chip assembly).
  • Experience in physical design working on advanced nodes.
  • Experience collaborating with cross-functional teams (e.g., architecture, RTL design, synthesis, verification).
  • 3D IC design experience (e.g., multi-die partitioning, TSV planning, advanced chiplet and packaging technologies, optimizing PPA, and physical verification in a SiP context).

Preferred qualifications:

  • Master's degree or PhD in Electrical Engineering, Computer Engineering, or Computer Science with an emphasis on computer architecture.
  • Experience in scripting languages (e.g., Python, Tcl, or Perl) and industry standard top-level tools including Innovus, FusionCompiler.
  • Experience working on various technologies (e.g., embedded processors, DDR, SerDes, HBM, networking-on-chip fabrics, etc.).
  • Experience using specialized EDA tools to resolve DRC/LVS/EMIR issues for leading edge nodes.
  • Experience with SoC design methodologies for full-chip power grid, global clocking, data path implementation, 3PIP integration, and bump planning.

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 creating the initial physical layout of a chip top-level; defining block sizing/placement, power grids, and clock distribution to meet performance, power, and area (PPA) goals; requiring deep collaboration with architecture, RTL, and synthesis teams; using industry and internal tools; and driving early timing/congestion closure for complex modern SoCs. You will utilize full-chip planning and IP integration, delivering floorplan collaterals and collaborating for signoff. This is a highly cross-functional and central role that will require interactions with numerous development teams.

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 $183,000-$271,000 + 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.

  • Own the planning, creation, and delivery of top-level floorplan deliverables and implementation for Silicon SOC projects from concept to working silicon volume.
  • Resolve structural or physical issues related to the integration of complex ASICs and SoCs, and collaborate with teams across Google to develop ideas for high impact silicon and hardware projects.
  • Manage all cross-functional interactions related to top-level floorplanning of chip projects.
  • Develop and improve floorplan implementation methodologies. Support and execute implementation flows using both industry-standard and specialized internal tools.
  • Perform technical evaluations of vendors and IP, providing recommendations and assessments of process node tradeoffs to meet performance, power, area, and cost goals.

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