Senior Quantum Applied Research Scientist, Calibration and Decoding at NVIDIA (USA)
Job Description
NVIDIA is hiring a Senior Quantum Applied Research Scientist for calibration and decoding, a role at the meeting point of quantum device physics, calibration and machine learning (requisition JR2019517).
At a glance
- Position: Senior Quantum Applied Research Scientist, Calibration and Decoding
- Where: Redmond, Washington; Santa Clara or remote in California, USA
- Pay: NVIDIA does not state a salary range in this posting; base salary depends on location and experience
- 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 calibration and decoding scientist role at NVIDIA
- Research and develop open AI models for quantum calibration and decoding
- Build physics-informed data synthesis pipelines and post-trainable model architectures
- Develop surrogate models and co-optimised calibration and decoding pipelines with practical benchmarks
- Translate qubit physics and the quantum control stack into AI systems for fault-tolerant quantum computing
The posting describes real-time models that learn from device physics, calibration experiments, decoding and system performance, and names physics-informed data synthesis, surrogate modelling and co-optimised calibration-decoding pipelines. 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 master’s degree in physics, computer science, electrical engineering, applied mathematics or a related field, with a PhD strongly preferred, or equivalent experience
- Eight or more years of combined, high-impact experience in quantum systems and AI/ML research
- Hands-on machine learning and deep learning for science or physics: architecture design, training at scale, fine-tuning and evaluation
- A strong background in quantum device physics, noise models, error mechanisms and fault-tolerant systems in one or more qubit modalities
- A broad understanding of quantum control, including pulse-level hardware interfaces and classical feedback
Nice to have
- Learned calibration or decoding models deployed inside real-time control loops
- Deep reinforcement learning experience: policy optimisation, reward shaping and sim-to-real transfer for physical systems
- Physics-informed or generative synthetic data generation, noise simulation or Hamiltonian learning
- Large-scale training and parameter-efficient fine-tuning methods
Pay and location
NVIDIA does not state a salary range in this posting; base salary depends on location and experience
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
- Control loops: prepare a clear account of a closed-loop experiment you have automated, and what limited its speed.
- Reinforcement learning: revise policy-gradient methods and sim-to-real transfer, which the posting singles out.
- Synthetic data: be ready to explain how you would generate physics-faithful training data for a decoder or calibrator.
- Qubit physics: know the main noise mechanisms for at least one platform and how calibration corrects them.
- Benchmarks: bring evidence that your models worked on hardware, not only in simulation.
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.
