Hardware/Electrical Engineering PhD Intern, 2026

Google Cloud and AI Infrastructure team in Israel is at the forefront of building next-generation systems through custom hardware development. As an intern, you'll collaborate with exceptional engineers and researchers on projects that directly impact Google's custom silicon portfolio, including general-purpose CPUs, AI accelerators like Tensor Processing Units (TPUs), and complex Systems-on-Chip (SoCs).

The role offers a unique opportunity to work at the intersection of hardware, software, and algorithms. You'll be involved in projects ranging from algorithmic innovation and software development to system architecture and hardware design. The work directly influences the future of Google Cloud and AI infrastructure by contributing to the foundational technology that powers everything from Google Cloud to advanced models like Gemini.

This internship is specifically designed for PhD students in their second or final year of education in Israel. The program requires a full-time commitment for 13-17 weeks during Summer 2026, starting in May, June, or July. You'll be working on-site in either Tel Aviv or Haifa, Israel, where you'll have the opportunity to make meaningful contributions to Google's hardware engineering initiatives.

The position demands strong technical skills, particularly in C++ programming, along with experience in data structures, algorithms, and architectural modeling. You'll be working in a collaborative environment where your research and engineering skills will be applied to real-world challenges in hardware and system design. This is an excellent opportunity for PhD candidates interested in bridging the gap between academic research and practical implementation in one of the world's leading technology companies.

The role offers exposure to cutting-edge technology and the chance to work on projects that will shape the future of cloud computing and AI infrastructure. You'll gain valuable industry experience while contributing to Google's mission of building more sustainable and efficient computing systems.