Intel
AI-Driven Physical Design Engineering (PhD Intern)
$142K–$142K
US, California, Santa Clara +4 · Internship · Posted Oct 9, 2026
- Location
- US, California, Santa Clara
US, Colorado, Fort Collins
US, Massachusetts, Beaver Brook
US, Oregon, Hillsboro
US, Texas, Austin - Education
- Enrolled in a PhD program in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
About the job
Develops AI/ML workflows and automation to improve physical design execution and optimize implementation of high-performance silicon.
What you'll do
- Research and implement AI/ML methods for physical design, and develop workflows that improve design quality and reduce manual effort.
- Build ML-powered data analysis pipelines and GNN-based systems for design decision-making.
- Benchmark AI-driven approaches against traditional methods for PPA improvements and collaborate with AI research and design teams.
What they ask for
- Enrolled in a PhD program in Electrical Engineering, Computer Engineering, Computer Science, or a related field.
- Proficiency with physical design tools for synthesis, place and route, and timing analysis.
- Familiarity with Python or TCL for flow automation and debugging.
- Understanding of digital design concepts and methodologies, including RTL design and verification.
Nice to have
- Understanding of Graph Neural Networks or graph-based algorithms; knowledge of reinforcement learning concepts for design optimization.
- Experience with data analysis tools (Pandas, NumPy, Matplotlib) and strong Python programming with ML application experience.
Pay
$141,998.00-142,002.00 USD (target compensation; base salary or total pay not specified)
Tools
Python, TensorFlow, PyTorch, TCL, Perl, Pandas, NumPy, Matplotlib
AI focus
Applies AI/ML, including GNN-based systems, to automate and optimize silicon physical design, analyze design data, and support implementation decisions.
Model-written summary · the original posting is authoritative