2 days ago
Base Salary
$184k - $357k/yr
Responsibilities
- Design, develop, and maintain quantum computing simulation components in CUDA-Q.
- Extend CUDA-Q with new simulation methods and capabilities for quantum dynamics and open-system simulation.
- Build connections between CUDA-Q programs and simulation libraries, including circuit and operator representations, execution interfaces, Python bindings, and resource management.
- Profile and improve end-to-end performance, memory usage, CPU–GPU data movement, and batching across GPU architectures and quantum workloads.
- Collaborate with cuQuantum SDK, CUDA-Q, NVIDIA Research, Product Management, external researchers, and open-source contributors.
- Lead technical work across research, engineering, and product teams.
Requirements
- Excellent modern C++ and Python programming skills, including debugging, functional testing, numerical validation, and performance profiling.
- Strong foundations in 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 collaborative software using Git, code review, automated builds, and continuous integration.
- Strong communication skills and experience leading technical work across research, engineering, and product teams.
- MS or PhD in Computer Science, Applied Mathematics, Physics, Electrical Engineering, or a related scientific or engineering field, or equivalent experience.
- At least 8 years of relevant research or software-development experience.
- Preferred experience with CUDA-Q, cuQuantum SDK, Qiskit Aer, Cirq, PennyLane, Stim, or QuTiP.
- Preferred expertise in stabilizer and Clifford simulation, sparse Pauli representations, Heisenberg-picture propagation, tensor-network methods, or quantum master-equation solvers.
- Preferred experience developing performance-critical GPU software and scaling simulation workloads across multiple GPUs or nodes using CUDA, MPI, NCCL, NVSHMEM, or comparable communication libraries.
- Preferred aptitude for using agentic or AI-supported development tools while maintaining validation, testing, and code review standards.
Benefits
- Hybrid work arrangement.
- Eligible for equity and benefits.
- Applications accepted at least until November 18, 2026.
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.