AI-Driven Physical Design Engineering; PhD Intern

Intel is seeking motivated individuals to join its dynamic design engineering team as an AI-Driven Physical Design Engineering PhD Intern. This role offers a unique opportunity to leverage artificial intelligence and machine learning techniques to revolutionize the physical implementation of cutting-edge silicon designs. You will focus on developing AI-powered workflows, building intelligent data analysis capabilities, and designing adaptive systems that enhance execution efficiency and optimize implementation strategies through data-driven decision making.

Job Description:

Intel is seeking motivated individuals to join its dynamic design engineering team as an AI-Driven Physical Design Engineering PhD Intern. This role offers a unique opportunity to leverage artificial intelligence and machine learning techniques to revolutionize the physical implementation of cutting-edge silicon designs. You will focus on developing AI-powered workflows, building intelligent data analysis capabilities, and designing adaptive systems that enhance execution efficiency and optimize implementation strategies through data-driven decision making.

The Data Center Group (DCG) is at the forefront of developing high-performance computing solutions that power the world’s data-centric applications. This team is dedicated to designing innovative silicon technologies that support Intel’s mission to lead in processing, storage, and networking solutions for data centers. Join a group committed to shaping the future of technology by solving complex challenges and driving performance enhancements.

• Research and implement AI/ML techniques to identify new opportunities for improving physical design processes and methodologies.
• Develop AI-enhanced workflows for high-performance silicon implementation that reduce manual effort and improve design quality.
• Build ML-powered data analysis and summarization pipelines to enable faster, more accurate design decision-making.
• Design and implement Graph Neural Network (GNN) based systems to support dynamic, intelligent decision-making in design execution.
• Develop intelligent automation frameworks using Python, and ML libraries such as Tensor Flow or PyTorch, alongside traditional scripting in TCL and Perl.
• Collaborate with cross-functional AI research and design engineering teams to align AI-driven strategies with broader project objectives.
• Evaluate and benchmark AI-driven methodologies against traditional approaches to demonstrate measurable improvements in PPA (Power, Performance, Area).

• Enrolled in a PhD program in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
• Proficiency in industry-standard tools for physical design, including synthesis, place and route, and timing analysis software.
• Familiarity with scripting languages like Python or TCL for flow automation and debugging.
• Understanding of digital design concepts and methodologies, including RTL design and verification.

• Strong problem-solving skills with a creative and innovative mindset.
• Ability to work effectively in a collaborative, team-oriented environment.
• Exceptional organizational and communication skills to drive alignment across diverse teams.
• Understanding of Graph Neural Networks (GNNs) or graph-based algorithms
• Experience with data analysis tools (Pandas, Num Py, Matplotlib)
• Knowledge of reinforcement learning concepts for design optimization
• Strong Python programming skills with ML application experience
• Background in EDA tools and physical design fundamentals

Student / Intern

Shift 1 (United States of America)

US, California, Santa…