Senior Quantum Applied Research Scientist, Calibration and Decoding

NVIDIA•Redmond, WA

1d•$192,000 - $304,750•Hybrid

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 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. At NVIDIA, we want to help accelerate the entire quantum ecosystem. 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! Do you love developing new technology, enjoy working with people and teams around the world, and operating at the speed of light? If yes, we would love to hear from you!

Requirements

Masters 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.

Nice To Haves

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.

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.