Computational Chemist - GPU Acceleration

PsiQuantum•Palo Alto, CA

72d•Remote

About The Position

PsiQuantum is seeking a Computational Chemist with strong GPU acceleration and scientific software development expertise to join our Quantum Solutions team. This role involves developing and optimizing computational chemistry algorithms and scientific workflows for modern heterogeneous computing platforms, with a particular emphasis on AMD and NVIDIA GPUs. The position is at the intersection of computational chemistry, electronic-structure theory, high-performance computing, and fault-tolerant quantum computing. A major focus will be 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. The successful candidate will collaborate closely with computational chemists, quantum algorithm researchers, and scientific software developers to identify performance bottlenecks, design scalable numerical algorithms, and translate state-of-the-art computational chemistry methods into robust, high-performance implementations. This role offers the opportunity to conduct publishable scientific research while developing computational capabilities that can operate at the scale required for industrially relevant chemistry and materials problems.

Requirements

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, atomistic simulation, or related numerical methods.
Demonstrated experience developing or optimizing GPU-accelerated scientific software.
Experience programming for GPU or heterogeneous computing environments, for example using CUDA, HIP/ROCm, or comparable GPU programming frameworks.
Experience working with NVIDIA and/or AMD GPU architectures and understanding of GPU performance considerations such as memory hierarchy, data movement, parallel execution, and kernel performance.
Strong experience with high-performance computing and parallel scientific workloads.
Proficiency in scientific programming and algorithm development using C++, C, Fortran, and/or Python, with the ability to work effectively in large scientific software codebases.
Experience profiling, benchmarking, and optimizing scientific applications.
Strong numerical and computational problem-solving skills and the ability to translate mathematical algorithms into efficient software implementations.
Ability to work effectively in a collaborative, interdisciplinary research environment spanning chemistry, physics, materials science, HPC, and quantum computing.

Nice To Haves

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 rather than individual kernels alone, including CPU/GPU scheduling, asynchronous execution, data movement, and distributed GPU workloads.
Experience with GPU-accelerated scientific libraries or frameworks for dense/sparse linear algebra, tensor operations, FFTs, or related numerical 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 with quantum embedding, localized orbital methods, active-space approaches, or reduced electronic models derived from first-principles calculations.
Experience applying or evaluating quantum-computing algorithms for electronic-structure, chemistry, or materials-science applications.
Experience developing portable performance-critical software targeting multiple accelerator architectures.
Strong track record of scientific software contributions and/or peer-reviewed publications.

Responsibilities

Develop, implement, and optimize computational chemistry and electronic-structure algorithms for GPU-accelerated computing platforms.
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 best-in-class 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 to address them.
Implement and optimize numerical methods relevant to electronic-structure calculations, including tensor contractions, linear algebra, iterative solvers, and related computational primitives.
Work closely with computational chemists and quantum algorithm researchers to benchmark and validate GPU-accelerated classical methods against emerging quantum algorithms.
Contribute to the architecture and development of scalable scientific software used for molecular and materials simulations.
Evaluate emerging GPU programming frameworks, libraries, and hardware capabilities and determine how they can improve computational chemistry workloads.
Participate in scientific collaborations and technical discussions across chemistry, physics, materials science, quantum computing, and high-performance computing teams.
Document software and research results and contribute to internal technical reports, scientific publications, and presentations.