Senior HPC Applications Engineer

Details

We are building a next-generation hybrid computing environment that merges large-scale HPC GPU clusters — anchored by an NVIDIA GB200 NVL72 system (572 GPUs) — with multiple quantum computing platforms. As an HPC Applications Engineer, you will work at the intersection of scientific research, high-performance computing, and quantum technologies to ensure advanced simulation, optimization, and AI-driven applications run efficiently, reliably, and scalably on this hybrid quantum-classical platform. You will partner closely with quantum researchers, software developers, and system engineers to deploy, profile, and tune applications that leverage both GPU acceleration and quantum backends.

Responsibilities

• Collaborate with quantum and domain scientists to install, configure, compile, and optimize research applications on the HPC + quantum environment.
• Profile and tune performance for GPU-accelerated and hybrid workloads using tools such as NVIDIA Nsight, nvprof, and CUDA-Q profilers.
• Optimize job execution and resource utilization via Slurm policies, GPU partitioning, and hybrid orchestration between classical and quantum nodes.
• Develop and maintain containerized environments (Singularity, Kubernetes, or Docker) to ensure reproducible builds and easy deployment.
• Advise researchers on parallelization strategies, CUDA kernels, MPI configurations, and scaling behaviors.
• Work with system engineers to validate firmware, driver, and library configurations that maximize application performance (e.g., CUDA, cuQuantum, cuBLAS, NCCL).
• Integrate quantum SDKs and simulators (e.g., CUDA-Q, Qiskit, or IonQ/QuEra APIs) into HPC workflows.
• Establish performance baselines and benchmarking suites for GPU and hybrid workloads; publish metrics and dashboards.
• Support and train users — from onboarding and code migration to advanced performance debugging — with a customer-first focus.
• Contribute to architecture evolution by providing feedback on workload patterns, bottlenecks, and future capacity planning.

Requirements

• 12+ years of experience in HPC application performance engineering, computational science, or scientific software development.
• Strong background in GPU programming (CUDA, cuQuantum, CUDA-Q) and parallel programming (MPI, OpenMP).
• Proficiency with Linux, Slurm, containerization, and CI/CD pipelines (GitHub, Jenkins, Ansible, or GitLab CI).
• Experience in profiling, benchmarking, monitoring, and optimizing scientific or AI/ML applications on multi-GPU systems.
• Working knowledge of NVIDIA HPC SDK, CUDA-Q, or cuQuantum stack.
• Bachelor’s or Master’s degree (or equivalent experience) in Computer Science, Physics, Applied Mathematics, or Engineering (PhD a plus).
• Excellent communication and collaboration skills to support a multidisciplinary research community.

Ways to Stand Out

• Exposure to other quantum computing frameworks.
• Experience optimizing multi-physics, molecular dynamics, or quantum chemistry codes.
• Demonstrated expertise in GPU-accelerated AI/ML model training and integration with scientific codes.
• Familiarity with hybrid workflow orchestration — combining HPC scheduling, quantum job APIs, and data movement pipelines.
• Contribution to open-source HPC or quantum software projects.

Compensation & Benefits

• Base salary range: 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 425,500 USD for Level 6.
• Eligible for equity and benefits (see company benefits page).

Additional Information

• Applications accepted at least until November 4, 2025.
• #LI-Hybrid