Quantum Hardware EDA & Circuit Design Intern - 2027

IBM Research is the innovation engine of IBM, advancing AI, hybrid cloud, quantum computing, and other technologies through collaborative research. The Quantum Hardware EDA & Circuit Design Intern will design, simulate, and optimize circuits and workflows for scalable quantum computing systems, collaborating on tools and approaches for qubit control, readout, packaging, and related quantum hardware.

What you'll do:

• As a Quantum Hardware EDA & Circuit Design Intern at IBM Quantum, you will contribute to the design, simulation, and optimization of circuits and design workflows that support scalable quantum computing systems
• You will collaborate with circuit designers, EDA developers, and researchers to develop practical tools and design approaches for qubit control, readout, packaging, and related quantum hardware

What they're looking for:

• Experience in an academic graduate program related to electrical engineering, microwave/RF engineering, electronic design automation, computer engineering, AI/ML, or a related field
• Coursework or projects in one or more of: microwave/RF circuits, electromagnetics, IC design/simulation, EDA, or AI for EDA
• Familiarity with Cadence Virtuoso, Synopsys Custom Compiler, and/or EDA design environments
• Programming experience in Python, or a comparable language for analysis, simulation, automation, EDA tool development, or machine-learning workflows
• Strong problem-solving skills and ability to communicate technical work clearly in a collaborative research environment
• Experience with microwave/RF circuit or component design, such as filters, amplifiers, resonators, transmission lines, matching networks, or digital place-and- route and associated EDA tools
• Familiarity with electromagnetic and microwave simulation tools, such as Ansys HFSS, Keysight ADS, Cadence AWR, Sonnet, or COMSOL
• Experience with EDA tools and workflows for schematic capture, simulation, layout, physical design, verification, or design automation
• Experience applying modern AI methods (including LLMs, reinforcement learning, graph neural networks, or Bayesian optimization) to EDA workflows such as circuit synthesis, simulation acceleration, design-space exploration, layout optimization, verification, or agent flow development
• Familiarity with Git/GitHub for version control and collaboration
• Strong publication record or contributions to papers in circuit design, EDA, or AI venues (e.g., ISSCC, IMS, DAC, ICCAD, NeurIPS, or ICML)
• Familiarity with AI-assisted design or optimization methods is a plus