IBM
Quantum Hardware EDA & Circuit Design Intern - 2027
Yorktown Heights, United States
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- On-site / unstated
- First seen by hirly
- 27 Sept 2026
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the posting
At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world. 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. Projects may span microwave and RF circuit design, electromagnetic simulation, circuit modeling, automation, and electronic design automation (EDA) methods, including the application of AI-enabled methods for circuit and system design, simulation, optimization, verification, and automation. 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. 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. Familiarity with AI-assisted design or optimization methods is a plus. 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) United States Research Internship Yorktown Heights, US Yorktown Heights (0147) International Business Machines Corporation Artificial Intelligence Automation Cadence Cadence Virtuoso Electronic Circuit Design Design Electronic Design Automation Electrical Engineering Engineering Git Github Integrated Circuit Design Machine Learning Microwave Optimization Physical Design Computer Programming Python Radio Frequency Research Schematic Capture Transmission Version Control Information Management System Hardware Computer Engineering Digital-to-analog Converter Electronic Design Automation Tools Large Language Model Synopsys Information Synthesis Ansys High-frequency Structure Simulator COMSOL Electromagnetism Networks Collaboration Reinforcement Learning Component Design Layout Optimization Graph Neural Networks
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