Google Hardware Engineering Intern – PhD, Summer 2026

Locations: Bengaluru, Karnataka, India; Hyderabad, Telangana, India
Application Deadline: March 31, 2026
Program Type: Internship
Duration: 12 weeks (full-time, outside university term)
Target Candidates: PhD students in Computer Engineering, Computer Science, Electronics & Communication, Electrical Engineering, or related technical fields

Minimum Qualifications

• Currently pursuing a PhD in Computer Engineering, Computer Science, Electronics & Communication, Electrical Engineering, or a related technical field

• Experience in one or more of the following areas:

  • Hardware System Integration

  • Signal and Power Integrity

  • System Validation

  • Wireless Communications

  • Product Design

  • Computer Architecture

  • Digital Design Verification

  • Digital Circuits

  • ASIC Physical Design

  • FPGAs

  • Embedded Systems

  • Memory Systems

• Programming experience in C++, Python, Verilog, UVM; familiarity with Synopsys and Cadence tools

Preferred Qualifications

• Experience with wireless communication interfaces and sensors

• Experience with performance modeling tools, Silicon design tools in Front End/Design Verification/Physical Design

• Knowledge of arithmetic units, bus architectures, accelerators, memory hierarchies, computer architecture; background in ML/DL

• Knowledge of high-performance and low-power design techniques

• Currently attending a degree program in India and available full-time for 12 weeks outside university term time

About the Program

• Work on the future of Google Cloud Silicon (e.g., TPUs, Arm-based servers, network products)

• Collaborate with hardware/software architects and designers to architect, model, analyze, define, and design next-gen Cloud Silicon

• Responsibilities may cover product definition, design, implementation, balancing performance, power, features, schedule, and cost

• Part of ML, Systems, and Cloud AI (MSCA) organization that designs and manages hardware, software, ML, and systems infrastructure for Google services and Cloud

• Focus on security, efficiency, and reliability in hyperscale computing

Responsibilities

• Vary based on team, may include design, modeling, testing, and implementation of hardware systems

• Collaborate across engineering teams to achieve optimal hardware solutions