Senior Software Engineer, Quantum Applied Research

NVIDIA•Redmond, WA

About The Position

At NVIDIA, we're solving the world's most exciting problems with our unique approach to accelerated computing. We're looking for a senior engineer at the intersection of quantum computing, distributed systems, and advanced data platforms. This role will help path-find the future of high-fidelity, real-time system modeling and co-design for quantum computing platforms. At NVIDIA, we want to help accelerate the entire quantum ecosystem. As a Senior Software Engineer, you will design and build data interfaces, orchestration layers, and system foundations that connect applications, simulation environments, AI models, and emerging computing systems. You will develop scalable data architectures, synthetic data generation pipelines, and agentic systems that enable advanced prediction, analysis, and co-design across complex systems. You will collaborate across Product, Engineering, and Applied Research to explore new platform capabilities for accelerated computing. Do you enjoy building new system abstractions, working across disciplines, and operating at the frontier of software, AI, and physics? If yes, we would love to hear from you!

Requirements

BS, MS, or PhD in Computer Science, Engineering, Physics, or a related field (or equivalent experience).
10+ years of experience building and operating production-grade software systems, with strong fundamentals in data engineering and distributed systems.
Working knowledge of quantum computing concepts and the ability to collaborate effectively with domain experts.
Hands-on experience with backend and data technologies (e.g., Python, C++, Go, APIs, distributed services, and modern data platforms).
Strong software engineering practices across testing, CI/CD, observability, and performance optimization.

Nice To Haves

Experience building simulation or modeling platforms in scientific computing, physics, or hardware-software environments.
Experience developing synthetic data pipelines or supporting AI/ML workflows at scale.
Familiarity with agentic systems, orchestration frameworks, or AI-driven automation.
Deeper exposure to quantum computing concepts, including hardware, control systems, or performance modeling.
Experience with GPU acceleration, CUDA, or high-performance computing for large-scale workloads.

Responsibilities

Build and evolve scalable data platforms and interfaces that integrate heterogeneous system data, simulation outputs, AI model inputs/outputs, and application workflows.
Design and implement agentic orchestration frameworks to support modeling, prediction, and coordination across complex computing systems.
Develop synthetic data generation pipelines and supporting infrastructure for training, validation, and evaluation of AI-driven models.
Implement robust data pipelines, storage systems, and APIs to support high-throughput, low-latency workloads across simulation and real-time environments.
Collaborate closely with research and engineering teams to translate domain-specific models and system signals into performant, reliable systems.