This role pays more than
85%
of similar roles. Most pay
$175,750–$225,575
— the shaded band above.
At the midpoint, this role pays about
$248k
versus about
$201k
for comparable roles.
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
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
As a Senior Quantum Applied Research Scientist, Calibration and Decoding, you will join the team to develop real-time models for fault-tolerant quantum hardware. You will build physics-informed data synthesis pipelines, surrogate models of quantum hardware, and co-optimized calibration-decoding pipelines that account for device physics and drift behavior. Your daily work involves developing open AI models for system calibration, creating synthetic training data from noise channels and Hamiltonian characterization, and applying reinforcement learning for policy optimization. You will utilize machine learning, deep learning, CUDA, and GPU programming to create scalable systems. The role addresses the technical challenge of translating qubit physics and quantum control stacks into performant AI systems. Key requirements include expertise in quantum device physics, information science, noise models, and large-scale model training using techniques like LoRA or QLoRA for scientific applications.
Develop physics-informed synthetic data generation pipelines using quantum device models and noise channels to create training data for calibration and decoding.
Build surrogate models of quantum hardware that capture device physics and drift behavior for rapid performance prediction.
Architect real-time AI systems that co-optimize model latency, throughput, and update cadence for fault-tolerant feedback loops.
Apply reinforcement learning and online learning methods to optimize calibration policies based on continuous hardware feedback.
Develop GPU-accelerated implementations to ensure the scalability of the entire quantum software pipeline.
Research and develop open AI models for quantum system calibration to establish shared foundations for the quantum community.
What we're looking for
Master's degree in Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field (Ph.D. 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 and training at scale.
Strong background in quantum device physics and information science, including noise models and fault-tolerant quantum systems.
Broad understanding of quantum control, including pulse-level hardware interfaces and classical feedback through software abstractions.
Experience with reinforcement learning applied to physical systems or closed-loop control problems.
Proficiency with CUDA and NVIDIA GPU programming for accelerating quantum simulation and AI model training.
Experience with physics-informed or generative approaches to synthetic data generation for scientific AI models.
PyTorch
NVIDIA NeMo
JAX
Hugging Face Transformers
Distributed Training
Reinforcement Learning
Multi-GPU
Multi-node
Isaac Sim
Isaac Lab
Nsight Systems
Nsight Compute
PyTorch Profiler
Data Pipelines
Model Optimization
Synthetic Data Generation
VLMs
VLAs