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This role pays more than
90%
of similar roles. Most pay
$174,937–$216,350
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At the midpoint, this role pays about
$248k
versus about
$196k
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Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1623 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 1110 roles with salary data.
The Senior Quantum Applied Research Scientist, Calibration and Decoding, joins the team to develop real-time models for fault-tolerant quantum hardware. This role involves designing physics-informed data synthesis pipelines, post-trainable model architectures, and practical benchmarks. You will build surrogate models capturing device physics and drift behavior, architect performant AI systems for joint calibration and decoding, and apply reinforcement learning and online learning methods for policy optimization. Key responsibilities include developing GPU-accelerated implementations and synthetic data generation leveraging noise channels and Hamiltonian characterization. The work addresses the technical challenge of translating qubit physics and quantum control stacks into performant AI systems. Required skills include deep learning for science, quantum device physics, noise models, and proficiency with CUDA and NVIDIA GPU programming for accelerating quantum simulation and real-time inference workloads.
Research and develop open AI models for quantum system calibration to empower the quantum community.
Build physics-informed synthetic data generation pipelines using quantum device models and noise channels.
Develop surrogate models of quantum hardware to capture device physics and predict performance.
Architect real-time AI systems that co-design model latency and throughput for fault-tolerant feedback loops.
Apply reinforcement learning and online learning methods to optimize calibration policies from hardware feedback.
Develop GPU-accelerated implementations to ensure the full pipeline scales for large-scale workloads.
Translate qubit physics and quantum control stacks into performant AI systems for fault-tolerant computing.
What we're looking for
Master's degree in Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field (Ph.D. strongly preferred).
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, and evaluation.
Strong background in quantum device physics and information science, including noise models, error mechanisms, and fault-tolerant quantum systems.
Broad understanding of quantum control, such as pulse-level hardware interfaces and classical feedback through software abstractions.
Excellent communication and collaboration skills.
Experience developing learned calibration or decoding models for real-time quantum control feedback loops (preferred).
Deep expertise in reinforcement learning applied to physical systems or closed-loop control problems (preferred).