NVIDIA
Overview
Join to apply for the
Senior HPC Applications Engineer
role at
NVIDIA . This role focuses on building a next-generation hybrid computing environment that merges large-scale HPC GPU clusters with 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 is to ensure that the most advanced simulation, optimization, and AI-driven applications run efficiently, reliably, and scalably on this 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.
Compensation and Benefits Base salary will be determined based on location, experience, and internal pay bands. Typical ranges may include USD 224,000–356,500 for Level 5 and USD 272,000–425,500 for Level 6. Equity and benefits are also available.
Equal Opportunity NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer. We value diversity in our current and future employees and do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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Join to apply for the
Senior HPC Applications Engineer
role at
NVIDIA . This role focuses on building a next-generation hybrid computing environment that merges large-scale HPC GPU clusters with 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 is to ensure that the most advanced simulation, optimization, and AI-driven applications run efficiently, reliably, and scalably on this 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.
Compensation and Benefits Base salary will be determined based on location, experience, and internal pay bands. Typical ranges may include USD 224,000–356,500 for Level 5 and USD 272,000–425,500 for Level 6. Equity and benefits are also available.
Equal Opportunity NVIDIA is committed to fostering a diverse work environment and is an equal opportunity employer. We value diversity in our current and future employees and do not discriminate on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
JR2006930
#J-18808-Ljbffr