Overview Join PsiQuantum to advance computational chemistry and quantum workflows at scale. You will develop GPU-accelerated algorithms and scientific software that bridge classical HPC and emerging quantum computing. Collaboration with chemists, quantum researchers, and software engineers will optimize end-to-end pipelines for industrial chemistry problems. The role offers publishable research opportunities while contributing to high-impact quantum solutions.
Compensation / Benefits- competitive health coverage
- 401(k) with company match
- equity grants
- wellness benefits
- family support programs
- paid leave programs
Responsibilities- Develop and optimize GPU-accelerated electronic-structure algorithms for CUDA, HIP/ROCm, and CPUs
- Design software to efficiently use NVIDIA and AMD GPUs and traditional HPC resources
- Profile, optimize kernels and memory movement to improve performance and scalability
- Build pipelines combining classical HPC/GPU methods with fault-tolerant quantum workflows
- Identify bottlenecks in electronic-structure and quantum-chemistry methods and create solutions
- Implement numerical methods for electronic-structure calculations (tensor contractions, linear algebra, iterative solvers)
- Collaborate with chemists and quantum researchers to benchmark classical methods against quantum algorithms
- Contribute to scalable scientific software for molecular and materials simulations
- Evaluate emerging GPU frameworks and hardware capabilities to improve workloads
- Engage in cross-disciplinary discussions across chemistry, physics, materials science, HPC, and quantum computing
- Document results and contribute to publications and technical reports
Key requirements- Ph.D. in related field or equivalent experience
- Strong understanding of computational chemistry or electronic-structure methods
- Experience developing or optimizing GPU-accelerated scientific software
- Experience programming for GPU or heterogeneous environments (CUDA, HIP/ROCm)
- Experience with NVIDIA and/or AMD GPU architectures and memory/parallelism considerations
- Strong HPC and parallel scientific workloads experience
- Proficiency in C++, C, Fortran, and/or Python
- Experience profiling and optimizing scientific applications
- Strong numerical problem-solving and translation of mathematical algorithms into software
- Ability to collaborate across disciplines in a research environment
- collaborative mindset
- strong communication
- problem-solving
- CUDA
- HIP/ROCm
- GPU-accelerated computing
