Senior Quantum Applied Research Scientist, Calibration and Decoding

Details

At NVIDIA, we're solving the world's most exciting problems with our unique approach to accelerated computing. We're looking for a passionate scientist at the intersection of quantum device physics, quantum calibration, and machine learning. This role will path-find the future of intelligent, real-time models for fault-tolerant quantum hardware.

As a Sr. Quantum Applied Research Scientist, you will help design and build real-time models that learn from device physics, calibration experiments, decoding, and system performance. You will develop physics-informed data synthesis pipelines, post-trainable model architectures, and practical benchmarks that the quantum community can build on. Your research will translate qubit physics and the quantum control stack into performant AI systems for fault-tolerant quantum computing. The work will span synthetic training data generation, surrogate modeling, and co-optimized calibration-decoding pipelines. You will collaborate with teams across Product, Engineering, and Applied Research to push the frontier of Accelerated Quantum Supercomputers.

Responsibilities

• Research and develop open AI models for quantum system calibration to advance the state of the art and empower the quantum community to build on shared foundations.
• Build physics-informed synthetic data generation pipelines that leverage quantum device models, noise channels, and Hamiltonian characterization to produce high-quality training data for upstream calibration and decoding model development.
• Develop surrogate models of quantum hardware that capture device physics and drift behavior, enabling rapid performance prediction and parameter inference without full experimental overhead.
• Architect performant real-time AI systems that jointly account for calibration state and decoding requirements, co-designing model latency, throughput, and update cadence to meet the demands of fault-tolerant feedback loops.
• Apply reinforcement learning and online learning methods to calibration policy optimization, enabling models that improve continuously from hardware feedback and generalize across device families and modalities.
• Develop GPU-accelerated implementations to ensure the full pipeline scales.
• Communicate research findings and collaborate with academic and industry partners to advance the field, while championing rapid innovation, technical depth, and creative problem solving.

Requirements

• Master's degree in Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field (Ph.D. strongly preferred); or equivalent experience.
• 8+ years of combined experience and high impact in quantum systems and AI/ML research.
• Hands-on expertise in machine learning and deep learning for science or physics, including model architecture design, training at scale, fine-tuning, and evaluation.
• Strong background in quantum device physics and information science, including noise models, error mechanisms, and fault-tolerant quantum systems across one or more qubit modalities.
• Broad understanding of quantum control, such as pulse-level hardware interfaces and classical feedback through software abstractions.
• Excellent communication and collaboration skills.

Ways to stand out

• Hands-on experience developing learned calibration or decoding models and deploying them within real-time quantum control feedback loops, with direct awareness of latency and throughput constraints.
• Deep expertise in reinforcement learning — including policy optimization, reward shaping, and sim-to-real transfer — applied to physical systems or closed-loop control problems.
• Experience with physics-informed or generative approaches to synthetic data generation, including noise simulation, Hamiltonian learning, or data augmentation for scientific AI models.
• Experience with large-scale model training and fine-tuning — including parameter-efficient methods (LoRA, QLoRA, adapters) and domain adaptation.
• Proficiency with CUDA and NVIDIA GPU programming for accelerating quantum simulation, AI model training, or real-time inference workloads at scale.

Compensation & Additional Information

• Base salary range: 192,000 USD - 304,750 USD (final base salary depends on location, experience, and peer pay).
• Eligible for equity and benefits (see http://www.nvidiabenefits.com/ and https://www.nvidia.com/en-us/benefits/).
• #LI-Hybrid
• Applications accepted at least until June 12, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes and is an equal opportunity employer.