Research Intern, AI and Quantum Algorithms for Optimization

What you'll do

• Design, implement, and analyze quantum, classical, and hybrid quantum-classical algorithms for optimization problems.
• Benchmark quantum optimization methods on IBM hardware and simulators using variational and non-variational techniques.
• Develop workflows that partition complex problems across QPUs and classical high-performance computing resources.
• Establish performance baselines against state-of-the-art solvers through complexity, scaling, and resource-estimation analyses.
• Explore AI and agentic systems for automated algorithm design, hyperparameter search, and experiment orchestration.
• Develop research prototypes using Python, Qiskit, and the scientific Python ecosystem with reproducible code.
• Produce technical reports, academic papers, patent disclosures, and contributions to open-source projects.

What we're looking for

• Bachelor's Degree required; Master's Degree preferred.
• Solid knowledge of quantum information, algorithms, and practical experience with Qiskit.
• Strong mathematical foundations in linear algebra, probability, discrete mathematics, and algorithm analysis.
• Proficiency in Python including scientific libraries like NumPy and SciPy.
• Demonstrated ability to frame problems, conduct rigorous experiments, and communicate results clearly.
• Depth in at least one area: model-based optimization, machine learning, quantum-centric supercomputing, or agentic AI.
• Experience with hardware execution, optimization solvers (Gurobi, CPLEX), or HPC environments (preferred).
• Publications, open-source contributions, and additional languages like C++, Julia, or Rust (preferred).