Senior Quantum AI Research Scientist, Applied Research at NVIDIA (Redmond or Santa Clara, USA)
Job Description
NVIDIA is hiring a Senior Quantum AI Research Scientist in its Applied Research group to build AI systems for fault-tolerant quantum computing, spanning quantum error correction, decoding and calibration (requisition JR2018110).
At a glance
- Position: Senior Quantum AI Research Scientist, Applied Research
- Where: Redmond, Washington, or Santa Clara, California, USA
- Pay: NVIDIA does not state a salary range in this posting; it says base salary depends on location, experience and internal comparisons
- Type: Full time, permanent
- Apply by: open until filled; re-check before applying
- Apply: open until filled (re-checked 16 October 2026)
About the Quantum AI Research Scientist role at NVIDIA
- Design and architect AI and machine-learning models, including deep neural networks and graph neural networks, for fault-tolerant quantum computing
- Research and develop open AI models, curated datasets and rigorous benchmarks that the quantum community can build on
- Fine-tune models for specific quantum error-correcting codes and hardware platforms
- Work with Product, Engineering and Applied Research teams to bring AI into NVIDIA’s accelerated quantum supercomputers
The posting says the work includes open AI models, curated datasets and benchmarks released for the wider quantum community, and fine-tuning models for specific quantum error-correcting codes and hardware platforms. NVIDIA states that applications for this job are accepted at least until the date given in the posting, which is a minimum rather than a closing date; the role stays listed until it is filled. NVIDIA also says it uses AI tools in its recruiting process.
What NVIDIA is looking for
- A degree in computer science, physics, applied mathematics, electrical engineering or a related field; NVIDIA strongly prefers a PhD and will consider equivalent experience
- Eight or more years of combined experience in quantum computing and AI/ML research, with high-impact work in at least one
- Deep expertise in machine learning and deep learning: model architecture design, training at scale and evaluation, applied to scientific or engineering problems
- A strong background in quantum information science, including quantum error correction, fault-tolerant protocols and quantum noise models
Nice to have
- Learned decoders or AI-driven calibration systems for real quantum hardware (superconducting qubits, trapped ions or other platforms)
- Large-scale model training and parameter-efficient fine-tuning such as LoRA, QLoRA or adapters for scientific models
- CUDA and GPU programming for quantum simulation, model training or real-time decoding
- High-performance computing and distributed training frameworks such as PyTorch Distributed, Megatron-LM or JAX
Pay and location
NVIDIA does not state a salary range in this posting; it says base salary depends on location, experience and internal comparisons
How to apply
Apply through the official NVIDIA job posting. NVIDIA’s posting does not give a closing date, so the role is open until filled; ResearchJobs.in will re-check this listing on 16 October 2026. Check the posting for location and work-authorisation details before you apply.
See all our quantum technology jobs, or browse more research jobs on ResearchJobs.in.
Hiring institution: NVIDIA
Official advertisement: nvidia.wd5.myworkdayjobs.com
How to prepare for this application
- Learn a decoder end to end: be ready to explain how a surface-code decoder works and where a neural network can beat matching-based decoding.
- Bring benchmarks: the role is about open models, datasets and benchmarks, so show work you have released or evaluated rigorously.
- Real-time constraints: prepare to discuss latency and throughput when a model sits inside a control loop.
- GPU fluency: revise CUDA basics and how you would profile and accelerate training or inference.
- Know the platform: read the CUDA-Q and cuQuantum documentation so you can talk about where your work would fit.
About NVIDIA's quantum computing group
NVIDIA builds accelerated computing hardware and software, and its quantum computing group works on the classical side of quantum computers: GPU-accelerated simulation, error-correction decoding, calibration and control. Its open platforms include CUDA-Q for hybrid quantum-classical programming and the cuQuantum SDK for simulation, and the group works with supercomputing centres and quantum hardware builders rather than building its own qubits.
