Embark on an exciting journey as a Silicon Engineering Intern shaping the future of Google Cloud Silicon, including TPUs, arm-based servers, and network products. Collaborate with leading hardware and software architects and designers to envision, model, analyze, and design next-generation Cloud Silicon.

This role offers dynamic, multi-faceted responsibilities across product definition, design, and implementation. You'll partner with engineering teams to achieve the optimal balance between performance, power, features, schedule, and cost. Join the ML, Systems, and Cloud AI (MSCA) organization, responsible for the hardware, software, ML, and systems infrastructure powering all Google services and Google Cloud.

We are a team that prioritizes security, efficiency, and reliability, driving the future of hyperscale computing. Google is fundamentally an engineering company, seeking individuals with broad technical skills ready to tackle significant technological challenges and make a global impact.

Responsibilities will vary depending on the specific teams you join. You will actively participate in the design, modeling, and analysis of next-generation Cloud Silicon. Key tasks include collaborating with architects and designers to define product specifications and drive optimal outcomes in performance, power, features, schedule, and cost.

The ideal candidate is currently pursuing a PhD in Computer Engineering, Computer Science, Electronics and Communication Engineering, Electrical Engineering, or a related technical field. Essential experience includes programming in languages such as C++, Python, Verilog, or UVM, along with proficiency in Synopsys and Cadence tools.

We are looking for experience in at least one of the following areas: hardware system integration, product design, computer architecture, digital design verification, digital circuits, ASIC physical design, FPGAs, embedded systems, or memory systems.

Preferred qualifications include enrollment in a degree program in India, availability for a 12-week full-time internship outside university terms, and experience with performance modeling tools, C++, Python, or silicon design tools (front-end/design verification/physical design). Knowledge in areas like arithmetic units, bus architectures, accelerators, memory hierarchies, computer architecture, linear algebra, or ML/DL is also beneficial, as is familiarity with high-performance and low-power design techniques.