Senior Quantum AI Research Scientist, Applied Research

NVIDIA NVIDIA · Semiconductors · Redmond, WA +1

NVIDIA is seeking a Senior Quantum AI Research Scientist to architect and build AI solutions for fault-tolerant quantum computing, focusing on quantum error correction, decoding, calibration, and beyond. The role involves researching and developing open AI models, datasets, and benchmarks, fine-tuning models for specific quantum systems, and collaborating with cross-functional teams to integrate AI into quantum supercomputers.

What you'd actually do

  1. 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.
  2. Develop cutting-edge AI techniques for quantum computing that contribute to NVIDIA's open model efforts across the quantum ecosystem.
  3. 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.
  4. Collaborate with quantum hardware teams to collect and structure hardware-derived training data, enabling domain-adapted models that improve over time as hardware matures.
  5. Co-design AI solutions with quantum hardware and software teams, ensuring decoders and calibration models meet latency and throughput requirements for real-time operation inside fault-tolerant feedback loops.

Skills

Required

  • Ph.D. strongly preferred
  • 8+ years of combined experience in quantum computing and/or AI/ML research
  • Deep expertise in machine learning and deep learning—including model architecture design, training at scale, and evaluation—applied to scientific or engineering problems.
  • Strong background in Quantum Information Science, including quantum error correction, fault-tolerant protocols, and quantum noise models.
  • Excellent communication skills
  • ability to collaborate effectively with multi-functional teams

Nice to have

  • Hands-on experience developing learned decoders or AI-driven calibration systems for quantum hardware (superconducting qubits, trapped ions, or other platforms).
  • Experience with large-scale model training and fine-tuning—including parameter-efficient fine-tuning (LoRA, QLoRA, adapters) and domain adaptation for scientific AI models.
  • Proficiency with CUDA and NVIDIA GPU programming for accelerating quantum simulation, AI model training, or real-time decoding workloads.
  • Experience with high-performance computing (HPC) environments and distributed training frameworks (e.g., PyTorch Distributed, Megatron-LM, or JAX pmap) for large-scale quantum AI workloads.

What the JD emphasized

  • track record of high-impact contributions
  • Deep expertise in machine learning and deep learning
  • Strong background in Quantum Information Science
  • Hands-on experience developing learned decoders or AI-driven calibration systems for quantum hardware
  • Experience with large-scale model training and fine-tuning

Other signals

  • architect and build AI solutions at the heart of fault-tolerant quantum computing
  • research and develop open AI models, curated datasets, and rigorous benchmarks
  • fine-tuning models for specific quantum error-correcting codes and hardware platforms