Computational Chemist - GPU Acceleration - Palo Alto, California, United States

Originally posted 27 August 2026 by the employer.

About the role

This role involves developing and optimizing computational chemistry algorithms and scientific workflows for modern heterogeneous computing platforms, with an emphasis on AMD and NVIDIA GPUs. A major focus is accelerating computationally demanding chemistry workloads, improving end-to-end computational pipelines, and enabling efficient integration between classical HPC/GPU calculations and emerging quantum-computing workflows.

What you'll do

• Design scientific software capable of efficiently utilizing NVIDIA and AMD GPU architectures, as well as conventional CPU-based HPC systems.
• Profile and optimize computational kernels, memory movement, parallelism, and end-to-end scientific workflows to improve performance and scalability.
• Develop computational pipelines that combine classical approaches, including HPC and GPU acceleration, with fault-tolerant quantum-computing workflows.
• Identify computational bottlenecks in electronic-structure and quantum-chemistry methods and develop algorithmic and software solutions.
• Implement and optimize numerical methods relevant to electronic-structure calculations, including tensor contractions, linear algebra, and iterative solvers.

What you'll need

• Ph.D. in computational chemistry, theoretical chemistry, computational physics, materials science, computer science, or a closely related field, or equivalent relevant experience.
• Strong understanding of computational chemistry or electronic-structure methods, such as density functional theory, wave-function-based quantum chemistry, or atomistic simulation.
• Experience programming for GPU or heterogeneous computing environments using CUDA, HIP/ROCm, or comparable GPU programming frameworks.
• Experience working with NVIDIA and/or AMD GPU architectures and understanding of GPU performance considerations.
• Strong experience with high-performance computing and parallel scientific workloads.
• Proficiency in scientific programming and algorithm development using C++, C, Fortran, and/or Python.
• Experience profiling, benchmarking, and optimizing scientific applications.

Nice to have

• Experience developing GPU implementations of electronic-structure or quantum-chemistry methods.
• Experience with both NVIDIA CUDA and AMD HIP/ROCm ecosystems, including porting or maintaining scientific software across GPU architectures.
• Experience optimizing large computational pipelines, including CPU/GPU scheduling, asynchronous execution, data movement, and distributed GPU workloads.
• Experience with distributed-memory HPC environments using technologies such as MPI in combination with multi-GPU computing.
• Experience developing, extending, or contributing to large-scale scientific software for electronic structure, quantum chemistry, or materials modeling.
• Experience with DMRG, tensor-network methods, or other strongly correlated electronic-structure methods, particularly implementations involving large tensor contractions or GPU acceleration.
• Experience with coupled-cluster, configuration-interaction, multireference, or other advanced wave-function-based electronic-structure methods.
• Experience applying or evaluating quantum-computing algorithms for electronic-structure, chemistry, or materials-science applications.
• Strong track record of scientific software contributions and/or peer-reviewed publications.

Skills: GPU acceleration, computational chemistry, CUDA, HIP/ROCm, high-performance computing