Silicon Engineering Intern, 2027

You will have the opportunity to collaborate with architects, implementation designers, verification engineers, and software engineers. The responsibilities may include but are not limited to developing architecture models, analysis tools, building simulation infrastructure, pushing the boundaries of performance and efficiency, and driving architectural decisions of our silicon products. The team that an intern may join is subject to project needs and intern's technical strength. Responsibilities may vary by specific team.

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.

Responsibilities

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

Minimum qualifications:

• Currently pursuing a Bachelor's, Master’s or PhD degree in Electrical Engineering, Computer Engineering, Computer Science, or related technical field.

• Penultimate academic year of the bachelor’s or master’s degree.

• Project experience in one of the following areas: computer architecture, microarchitecture, RTL design, design verification: creating testbenches and validation plans, formal or assertion-based verification, physical design and circuits: synthesis, place-and-route, timing closure, custom circuit design, or power/signal integrity analysis, pre/post-silicon validation.

• Experience with Python, C or C++.

Preferred qualifications:

• Experience with performance modeling, system-level simulation, or embedded systems development.
• Expertise in a specialized area, such as high-performance/low-power design techniques, memory hierarchies, machine learning accelerators, interconnects, or advanced packaging.
• Familiarity with industry-standard EDA tools (e.g., from Synopsys, Cadence, Mentor).
• Available to work full time for 10-12 weeks outside of university term time. In their penultimate academic year and returning to a degree program after completion of the internship.
• Strong foundation in design automation (CAD/EDA), including developing and supporting design flows and infrastructure for large-scale projects.