Sr. HPC Engineer - Platforms

PsiQuantum • Palo Alto, CA

8d

About The Position

PsiQuantum is hiring an HPC Software Engineer to design, develop, and optimize high-performance computing software using NVIDIA GPUs, AMD CPUs, and MPI for distributed and parallel computing. The role requires a strong foundation in computational science, parallel algorithms, and GPU and CPU optimization. The engineer will help design and implement scalable solutions for HPC clusters so applications run efficiently as nodes scale to support expanding workloads.

The position includes developing HPC software, applying parallel and concurrent computing techniques, contributing to cluster design and scaling, optimizing algorithms, profiling and benchmarking applications, and collaborating with scientists, engineers, and IT staff. The engineer will document code and scaling processes and report performance metrics and results to stakeholders.

Requirements

• Bachelor's or master's degree in computer science, Electrical Engineering, Computational Science, or a related field.
• At least 3 years of HPC development experience focused on parallel and distributed computing, including experience with scalable systems.
• At least 5 years of previous scientific software development experience.
• Proficiency in CUDA and familiarity with NVIDIA's HPC software stack, including cuDNN, NCCL, and TensorRT.
• Expertise in MPI, OpenMP, and other parallel programming paradigms.
• Strong understanding of cluster scaling and software optimization for distributed, multi-node environments.
• Strong proficiency in C/C++, Python, and Ansible, Terraform, or Packer.
• Familiarity with profiling and performance optimization tools such as NVIDIA Nsight, AMD μProf, and Intel VTune.
• Ability to work independently and collaboratively, strong problem-solving abilities, strong communication skills, and attention to detail.

Nice To Haves

• Advanced degrees or relevant certifications.
• Experience with Slurm, PBS, or other job scheduling and resource management tools.
• Knowledge of Singularity or Docker in an HPC context.
• Understanding of cloud-based HPC scaling solutions or hybrid HPC environments.
• Experience with GPU-accelerated machine-learning frameworks such as TensorFlow, PyTorch, or PySCF.

Responsibilities

• Design and implement software solutions using NVIDIA GPUs and AMD CPUs, maximizing computational efficiency and speed.
• Apply MPI, CUDA, OpenMP, and other parallel computing techniques to scale applications across multi-core and multi-GPU systems.
• Contribute to the design, deployment, and scaling of HPC nodes and optimize software and cluster configurations for seamless scaling.
• Identify and implement algorithms suited to GPU and CPU computing for complex computations and data-intensive tasks, maintaining performance as node counts increase.
• Profile and benchmark HPC applications across scaled environments, identify bottlenecks, and tune performance using tools such as NVIDIA Nsight, AMD μProf, and Intel VTune.
• Collaborate with scientists, engineers, and IT staff to ensure applications are optimized and scalable for research and production environments.
• Document code and scaling processes, and report performance metrics, improvements, and results to stakeholders.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior