Silicon Engineering Intern, PhD, Summer 2027

The Google Cloud and AI Infrastructure team focuses on building Google's next-generation systems. The team's work is grounded in creating custom hardware, and as an intern you'll get to collaborate with engineers from various parts of the organization to influence the future of Google Cloud and the AI infrastructure by working on the foundational technology that makes it all possible. This is the hardware that powers everything from Google Cloud, to our most advanced models like Gemini. As an intern, you will work on fundamental problems at the intersection of hardware, software, and algorithms. You will collaborate with exceptional engineers and researchers on projects that directly impact Google's custom silicon portfolio, including highly specialized AI accelerators like our Tensor Processing Units (TPUs), and complex Systems-on-a-Chip (SoCs). Your responsibilities and projects will be tailored to your expertise and could focus on any part of the system stack. Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems. Google is and always will be an engineering company. We hire people with a broad set of technical skills who are ready to address some of technology's greatest challenges and make an impact on millions, if not billions, of users. At Google, engineers not only revolutionize search, they routinely work on massive scalability and storage solutions, large-scale applications and entirely new platforms for developers around the world. From Google Ads to Chrome, Android to YouTube, Social to Local, Google engineers are changing the world one technological achievement after another. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $110000 - $147000 (USD) + 0% bonus target Learn more about benefits at Google .

Responsibilities and detailed projects will be determined based on your educational background, interest, and skills.

Minimum qualifications: Currently pursuing a PhD degree in Electrical Engineering, Computer Engineering, Computer Science, or a related technical field. Project or coursework experience in any one of the silicon engineering areas: architecture and RTL, verification and validation, physical design and circuits, systems, test and CAD. Experience with scripting and programming languages such as Python, Tcl, Perl, or C++. Preferred qualifications: Currently attending a degree program in the US and available to work full time for 12 weeks outside of university terms, and also in penultimate academic year or returning to a degree program after completion of the internship. Research experience in specialized areas such as high-performance/low-power architectures, domain-specific accelerators (DSAs/TPUs), memory hierarchies, coherent interconnects (e.g., CXL, PCIe, UCIe, NoC), or advanced packaging/chiplets. Experience developing and maintaining CAD/EDA design flows, methodology infrastructure, or applying ML for chip design automation. Understanding of advanced ML/DL model architectures (e.g., Transformers, LLMs) with experience in performance modeling, cycle-accurate simulation, or custom AI accelerator evaluation. Familiarity with industry-standard EDA tools (e.g., from Synopsys, Cadence, Siemens EDA, or emulation/formal platforms). Demonstrated research track record and ability to work on complex, open-ended problems, with publications in conferences (e.g., ISCA, MICRO, HPCA, ASPLOS, DAC, ICCAD, ISSCC, NeurIPS, MLSys).