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,…