Quantum System Performance Monitoring Intern 2027

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.

You will join a team of scientists working to improve the performance, stability, and availability of quantum computing systems through automated monitoring and calibration. In this role, you will help develop and improve techniques for identifying quantum processor performance drift and degradation, making automated decisions about when to recalibrate or take other actions, and optimizing system parameters.

Depending on your background and the needs of the project, this may include applying machine learning and AI techniques, statistical analysis, optimization methods, or other data-driven approaches to monitoring and calibration.

Required Skills

• Undergraduate-level familiarity with quantum physics and quantum computing, Python programming, numerical analysis and statistics.
• Graduate-level familiarity and/or hands-on real-world experience with many of the above.
• Hands-on experience with experimental quantum systems, quantum-device characterization, or calibration.
• Experience using the Qiskit Python package.
• Experience developing or applying machine-learning, statistical, or optimization methods to experimental or time-series data.
• Familiarity with anomaly detection, statistical inference, automated control, or parameter optimization.
• Experience working with large experimental datasets or data-analysis pipelines.