Research Intern 2027 – GPU Acceleration for Quantum Computing Applications

IBM Research advances AI, hybrid cloud, high-performance computing, and quantum computing through cutting-edge computational research. The Research Intern will accelerate and scale hybrid HPC–quantum applications by analyzing and parallelizing sequential code, offloading workloads to GPUs, evaluating performance at scale, and collaborating with quantum scientists and HPC experts.

What you'll do:

• Analyze sequential code to identify opportunities for parallelization using shared-memory and distributed-memory techniques
• Identify portions of the resulting code that can be offloaded to GPUs for improved performance
• Run the code at scale (up to hundreds of GPUs) and evaluate performance and scalability
• Work alongside quantum scientists to understand the impact of your work on their research: will this allow larger problems to be solved, and make the new algorithm competitive against state-of-the-art classical algorithms?

What they're looking for:

• Proficiency in Python and experience with modern software engineering practices
• Proficiency in at least one systems programming language (e.g., C, C++, Rust)
• Some level of hands-on experience with HPC software (e.g., OpenMP, MPI, CUDA)
• Ability to communicate technical results clearly to both HPC engineers and domain scientists, and to work independently in a research setting
• Master's Degree
• Experience developing and running parallel or distributed applications on a multi-node HPC cluster, including job schedulers such as Slurm or LSF
• Hands-on GPU programming experience with CUDA, HIP, or a portability layer such as OpenMP target offload, Kokkos, or OpenACC
• Familiarity with performance analysis and profiling tools (e.g., Nsight Systems/Compute, VTune, TAU, HPCToolkit) and with reasoning about roofline, strong/weak scaling, and communication bottlenecks
• Experience with GPU-accelerated numerical libraries (e.g., cuBLAS/cuSOLVER, cuTENSOR, MAGMA, NCCL) or scientific Python stacks such as NumPy/SciPy, CuPy, JAX, PyTorch, mpi4py, Dask, or Numba
• Background in numerical linear algebra, tensor networks, or Monte Carlo methods, and experience with computational chemistry, materials science, or condensed matter physics codes
• Exposure to quantum computing concepts and frameworks such as Qiskit — including quantum error mitigation or quantum error correction — or a demonstrated interest in learning them
• Experience bridging Python and compiled code (e.g., pybind11, nanobind, Cython, CFFI) to accelerate research prototypes

Benefits:

• Unique opportunities to develop software supporting the future of compute while growing professionally within a team
• Quantum research scientists and HPC experts will guide you throughout the project