Senior HPC and Quantum Systems Engineer

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world.

Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

• Hybrid Quantum–HPC Platform Engineering
  • Build, deploy, and operate a hybrid computing platform combining large‑scale NVIDIA GPU clusters with physical quantum processors (neutral atom, trapped ion, superconducting, and future modalities).
  • Integrate quantum control systems and access nodes with HPC infrastructure using APIs, middleware, and orchestration frameworks such as CUDA‑Q, cu Quantum, NVQlink, and related tool chains.
  • Develop and refine hybrid execution workflows that coordinate GPU computation, quantum execution, and data movement across tightly coupled systems.
• HPC Systems & Operations
  • Be responsible for the reliability, performance, and lifecycle management of the Quantum environment, including Linux systems, QPU hardware, job schedulers (e.g., Slurm), networking, and storage.
  • Work closely with networking and facilities teams to ensure power, cooling, timing, environmental controls, and low‑latency connectivity meet quantum hardware requirements.
• Vendor & Partner Collaboration
  • Serve as a technical interface to quantum hardware partners, collaborating on system bring‑up, connectivity, control interfaces, and co‑design opportunities.
  • Help define hosting, networking, and integration requirements for new quantum systems entering the facility.
• Application Enablement & Performance
  • Partner with internal researchers and engineers to deploy, optimize, and benchmark hybrid workloads across simulation, quantum error correction, calibration, optimization, and machine learning.
  • Prototype and evaluate end‑to‑end workflows that demonstrate the capabilities of the platform.
• Technical Leadership
  • Produce clear internal documentation, integration guides, and operational runbooks to enable broader adoption of the platform.
  • Represent the organization at technical workshops, conferences, and industry forums when appropriate.

• 12+ years of hands‑on experience operating HPC or large‑scale compute infrastructure in production environments.
• Bachelor’s degree or Master’s degree with equivalent experience in Physics, Electrical/Computer Engineering, Computer Science, or equivalent practical experience (PhD a plus).
• Strong background in Linux systems administration, job schedulers (Slurm or equivalent), and computing environments committed to reliable operation.
• Proven experience working in data center environments, including networking, storage, power, and operational constraints.
• Familiarity with quantum computing concepts and hardware architectures (neutral atom, trapped ion, superconducting, photonic).
• Experience with or strong interest in quantum software environments including CUDA‑Q, Qiskit, Cirq, Penny Lane, Braket, or similar.
• Solid understanding of GPU‑accelerated computing and heterogeneous systems.
• Proficiency in Python and/or C++ for automation, API integration, and workflow orchestration.
• Comfort working across system boundaries: control systems, APIs, networking, and performance tooling.
• Strong communication skills and the ability to work effectively with experimental scientists, systems engineers, and external partners.

• Direct experience integrating or hosting physical quantum hardware in a data center or HPC environment.
• Deep knowledge of quantum–classical orchestration, low‑latency data paths, or real‑time control considerations.
• Experience working with NVIDIA tools and frameworks.
• Experience bridging experimental research teams and production infrastructure.

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