Senior Engineer - Quantum Error Correction Libraries

NVIDIA's accelerated computing platform has revolutionized HPC and AI, and CUDA-Q brings accelerated computing to the quantum computing ecosystem. We are looking for a senior software engineer to develop, scale, and optimize GPU-accelerated simulators for CUDA-Q, our platform for hybrid quantum-classical computing. This role combines modern C++ and Python development with quantum computing expertise to turn cuQuantum SDK primitives into robust, high-performance simulation capabilities. Ideal candidates will have experience building scientific software and a strong curiosity about advancing quantum circuit and quantum dynamics simulation. If you are passionate about quantum simulation software and want to help researchers explore quantum computing at scale, we would love to hear from you! What you will be doing:

• Designing, developing, and maintaining quantum computing simulation components in CUDA-Q.
• Extending CUDA-Q with new simulation methods, optimizing existing, and exposing new capabilities for quantum dynamics and open-system simulation.
• Building the software that connects CUDA-Q programs to simulation libraries, including circuit and operator representations, execution interfaces, Python bindings, and resource management.
• Profiling and improving end-to-end performance, memory use, CPU–GPU data movement, and batching across GPU architectures and representative quantum workloads.
• Collaborating with cuQuantum SDK, CUDA-Q, NVIDIA Research, and Product Management teams, as well as external researchers and open source contributors, to translate use cases into reliable software.

What we need to see:

• Excellent modern C++ and Python programming skills, including debugging, functional testing, numerical validation, and performance profiling.
• Strong quantum computing foundations, including quantum circuits, linear algebra, measurement, observables, density matrices, and quantum noise channels.
• Hands-on experience implementing or extending quantum simulators, with depth in at least one approach such as state-vector, tensor-network, stabilizer, Pauli propagation, or open-system dynamics simulation.
• Experience developing maintainable software in a collaborative environment using Git, code review, automated builds, and continuous integration.
• Strong communication skills and experience leading technical work across research, engineering, and product teams.
• PhD or MS degree in Computer Science, Applied Mathematics, Physics, Electrical Engineering, or a related scientific or engineering field, or equivalent experience.
• 8+ years of relevant research or software-development experience.

Ways to stand out from the crowd:

• Experience with CUDA-Q, cuQuantum SDK, or comparable frameworks and simulators such as Qiskit Aer, Cirq, PennyLane, Stim, or QuTiP.
• Expertise in stabilizer and Clifford simulation, sparse Pauli representations and Heisenberg-picture propagation, tensor-network methods, or quantum master-equation solvers.
• Experience developing performance-critical GPU software with CUDA and scaling simulation workloads across multiple GPUs or nodes using MPI, NCCL, NVSHMEM, or comparable communication libraries.
• Aptitude for using agentic or AI-supported development tools while maintaining detailed validation, testing, and code review standards.

#LI-Hybrid Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 184,000 USD – 287,500 USD for Level 4; and 224,000 USD – 356,500 USD for Level 5. You will also be eligible for equity and benefits. Applications for this job will be accepted at least until November 16, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) 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.