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 our HPC Application Engineer, you’ll work at the intersection of scientific research, high-performance computing, and quantum technologies. Your mission: ensure that the most advanced simulation, optimization, and AI-driven applications run efficiently, reliably, and scalably on this new hybrid quantum-classical platform. You’ll partner closely with quantum researchers, software developers, and system engineers to deploy, profile, and tune applications that leverage both GPU acceleration and quantum backends.
What You’ll Be Doing:
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. Customer first focus.
Contribute to architecture evolution by providing feedback on workload patterns, bottlenecks, and future capacity planning.
What We Need to See:
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 from the crowd:
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.
This role is critical to unlocking the full potential of our hybrid quantum-HPC architecture. You’ll empower researchers to explore new frontiers in quantum design, life sciences, and AI-driven discovery — ensuring that every computation, classical or quantum, runs at its highest efficiency.
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 224,000 USD - 356,500 USD for Level 5, and 272,000 USD - 425,500 USD for Level 6.You will also be eligible for equity and benefits.