# Senior Quantum Applied Research Scientist, Calibration and Decoding

**Company**: NVIDIA
**Work arrangement**: hybrid
**Experience**: senior
**Job type**: full-time
**Category**: Engineering
**Industry**: Technology

**Apply**: https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-WA-Redmond/Senior-Quantum-Applied-Research-Scientist--Calibration-and-Decoding_JR2019517?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_66c617f2-e94

## Description

"" At NVIDIA, 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:**  * 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 Have:**  * 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. ""

## Skills

### Required
- Quantum device physics
- Machine learning
- Deep learning
- Quantum control
- Reinforcement learning
- CUDA
- NVIDIA GPU programming

### Nice to have
- Physics-informed synthetic data generation
- Surrogate modeling
- Co-optimized calibration-decoding pipelines
- GPU-accelerated implementations
- Large-scale model training and fine-tuning

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Source: [Apply at nvidia.wd5.myworkdayjobs.com](https://nvidia.wd5.myworkdayjobs.com/en-US/NVIDIAExternalCareerSite/job/US-WA-Redmond/Senior-Quantum-Applied-Research-Scientist--Calibration-and-Decoding_JR2019517?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)