Senior Quantum Applied Research Scientist, Calibration and Decoding

Details

At NVIDIA, we are solving the world's most exciting problems with a unique approach to accelerated computing. This role sits at the intersection of quantum device physics, quantum calibration, and machine learning, helping develop intelligent, real-time models for fault-tolerant quantum hardware.

The scientist will design and build real-time models that learn from device physics, calibration experiments, decoding, and system performance. The work includes physics-informed data synthesis pipelines, post-trainable model architectures, practical benchmarks, surrogate modeling, and co-optimized calibration-decoding pipelines. Research will translate qubit physics and the quantum control stack into performant AI systems for fault-tolerant quantum computing. The role involves collaboration with Product, Engineering, Applied Research, and academic and industry partners.

Responsibilities

• Research and develop open AI models for quantum system calibration.
• Build physics-informed synthetic data generation pipelines using quantum device models, noise channels, and Hamiltonian characterization to produce training data for 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 account jointly for calibration state and decoding requirements, including model latency, throughput, and update cadence for fault-tolerant feedback loops.
• Apply reinforcement learning and online learning methods to calibration policy optimization, enabling models to improve continuously from hardware feedback and generalize across device families and modalities.
• Develop GPU-accelerated implementations so the full pipeline can scale.
• Communicate research findings and collaborate with academic and industry partners to advance the field.

Requirements

• Master's degree in Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field; a Ph.D. is strongly preferred. Equivalent experience may be considered.
• At least 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, including pulse-level hardware interfaces and classical feedback through software abstractions.
• Excellent communication and collaboration skills.

Preferred Qualifications

• Experience developing learned calibration or decoding models and deploying them within real-time quantum control feedback loops, with 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 such as LoRA, QLoRA, and adapters, as well as domain adaptation.
• Proficiency with CUDA and NVIDIA GPU programming for accelerating quantum simulation, AI model training, or real-time inference workloads at scale.

Compensation and Benefits

The base salary range is USD 192,000–304,750. Employees are also eligible for equity and benefits. NVIDIA offers a comprehensive benefits package.

NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.