Nvidia

SeniorQuantumAIResearchScientist,AppliedResearch

$192–305k Redmond, Washington, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Quantum AI Research Scientist, Applied Research at Nvidia. Skills: Quantum computing, AI/ML, Deep learning, Quantum error correction. Architect AI/ML models for quantum error correction. Develop AI techniques for quantum computing”

What You'll Achieve.

Advance the state of the art; Empower the broader quantum community; Improve over time as hardware matures

Industry & Context.

Problems you'll solve

Creative problem solving

What They're Looking For.

Must Have

Degree in Computer Science, Physics, Applied Mathematics, Electrical Engineering, or related field, 8+ years of combined experience in quantum computing and/or AI/ML research, Deep expertise in machine learning and deep learning, Background in Quantum Information Science

Nice to Have

Ph. D. strongly preferred, Hands-on experience developing learned decoders or AI-driven calibration systems for quantum hardware, Experience with large-scale model training and fine-tuning, Proficiency with CUDA and NVIDIA GPU programming, Experience with high-performance computing (HPC) environments, Experience with distributed training frameworks

What You'll Do.

Architect AI/ML models for quantum error correction

Develop AI techniques for quantum computing

Create large-scale datasets for quantum error correction

Help create datasets for quantum system characterization

Collaborate with quantum hardware teams

Collect and structure hardware-derived training data

Co-design AI solutions with quantum hardware and software

Communicate research findings

Collaborate with academic and industry partners

How You'll Work.

Team & Collaboration

Multi-functional teams; Product teams; Engineering teams; Applied Research teams; Quantum hardware teams; Quantum software teams; Academic partners; Industry partners

Communication Scope

Communicate research findings

Full Job Description

At NVIDIA, we're solving the world's most exciting problems with our unique approach to accelerated computing. We're looking for a passionate AI research scientist with deep quantum computing expertise to path-find the future of fault-tolerant quantum systems powered by machine learning. Quantum computing is a strategic priority for NVIDIA, and our goal is to accelerate the entire ecosystem. As a Sr. Applied Research Scientist in Quantum Computing, you will architect and build AI solutions at the heart of fault-tolerant quantum computing—spanning quantum error correction, decoding, calibration, and beyond. You will research and develop open AI models, curated datasets, and rigorous benchmarks that advance the state of the art and empower the broader quantum community. Your research will help translate cutting-edge theory into practice by fine-tuning models for specific quantum error-correcting codes and hardware platforms, while collaborating with multi-functional teams across Product, Engineering, and Applied Research to integrate AI into next-generation Accelerated Quantum Supercomputers! Do you love developing new technology, enjoy working with collaborative people and teams around the world, and operating at the speed of light? If yes, we would love to hear from you! **What you 'll be doing:** * Design and architect AI/ML models—including deep neural networks, graph neural networks, transformers, and reinforcement-learning agents—for quantum error correction, syndrome decoding, logical operation synthesis, and real-time calibration in fault-tolerant quantum systems. * Develop cutting-edge AI techniques for quantum computing that contribute to NVIDIA's open model efforts across the quantum ecosystem. * Help create high-quality, large-scale datasets for quantum error correction and quantum system characterization, including simulated and hardware-derived syndrome data, enabling the community to train and evaluate AI models at scale. * Collaborate with quantum hard

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