Senior Quantum AI Research Scientist, Applied Research

Nvidia

Confirmed live yesterday High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Redmond, WASanta Clara, CA
Salary: $192,000–$304,750 / yr
Posted: 113 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $218k

This role $248k

$156k most similar roles pay here $321k

This role pays more than 72% of similar roles. Most pay $180,462–$254,750 — the shaded band above. At the midpoint, this role pays about $248k versus about $218k 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.

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At a glance

TL;DR · Senior Quantum AI Research Scientist, Applied Research

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As a Senior Quantum AI Research Scientist, Applied Research, you will join the team to path-find the future of fault-tolerant quantum systems powered by machine learning. You will architect and build AI solutions for quantum error correction, decoding, calibration, and logical operation synthesis. Your daily work involves designing models such as deep neural networks, graph neural networks, transformers, and reinforcement-learning agents while developing curated datasets and rigorous benchmarks. You will utilize technologies including CUDA, NVIDIA GPU programming, PyTorch Distributed, Megatron-LM, and JAX pmap to accelerate quantum simulation and training. The role focuses on the technical challenge of translating cutting-edge theory into practice by fine-tuning models for specific error-correcting codes and hardware platforms. You will collaborate with multi-functional teams to ensure decoders meet latency requirements for real-time operation within fault-tolerant feedback loops.

Skills

Quantum Computing Machine Learning Deep Learning Neural Networks Graph Neural Networks Transformers reinforcement-learning Quantum Error Correction CUDA NVIDIA GPU PyTorch JAX Megatron-LM LoRA QLoRA HPC Distributed Training

What you'll do

Design and architect AI/ML models like neural networks and reinforcement learning for quantum error correction and calibration.
Develop cutting-edge AI techniques to contribute to NVIDIA's open model efforts across the quantum ecosystem.
Create high-quality, large-scale datasets from simulated and hardware-derived data for training and evaluating AI models.
Collaborate with hardware teams to collect and structure training data for domain-adapted quantum models.
Co-design AI solutions that meet specific latency and throughput requirements for real-time operation in fault-tolerant feedback loops.
Communicate research findings through top-tier venues and collaborate with academic and industry partners.

What we're looking for

Degree in Computer Science, Physics, Applied Mathematics, Electrical Engineering, or a related field (Ph.D. strongly preferred).
8+ years of combined experience in quantum computing and/or AI/ML research with a track record of high-impact contributions.
Deep expertise in machine learning and deep learning including model architecture design, training at scale, and evaluation for scientific problems.
Strong background in Quantum Information Science, specifically quantum error correction, fault-tolerant protocols, and quantum noise models.
Experience developing learned decoders or AI-driven calibration systems for quantum hardware platforms.
Proficiency with CUDA and NVIDIA GPU programming to accelerate quantum simulation and AI model training.
Experience with high-performance computing (HPC) environments and distributed training frameworks like PyTorch Distributed, Megatron-LM, or JAX.
Excellent communication skills and the ability to collaborate effectively with multi-functional teams across research, engineering, and product.

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