Applied Research Scientist, Calibration & Decoding

Job Description

Job Title: Senior Quantum Applied Research Scientist, Calibration and Decoding. Location:

• Redmond, Washington, USA
• Santa Clara, California, USA
• Remote, California, USA

Responsibilities:

• Research and develop open AI models for quantum system calibration.
• Build physics-informed synthetic data generation pipelines using quantum device models, noise channels, and Hamiltonian characterization.
• Develop surrogate models of quantum hardware that capture device physics and drift behavior.
• Build real-time AI systems that jointly account for calibration state and decoding requirements.
• Co-design model latency, throughput, and update cadence for fault-tolerant feedback loops;
• Apply reinforcement learning and online learning to optimize calibration policies.
• Develop GPU-accelerated implementations so the complete pipeline can scale.
• Communicate research findings and collaborate with academic and industry partners.
• Contribute to rapid innovation, technical depth, and creative problem solving.

Qualifications:

• Master’s degree in Physics, Computer Science, Electrical Engineering, Applied Mathematics, or a related field. A PhD is strongly preferred, or equivalent experience.
• 8+ years of combined experience and high-impact work in quantum systems and AI/ML research.
• Hands-on expertise in machine learning and deep learning for science or physics, including model architecture design, large-scale training, fine-tuning, and evaluation.
• Strong background in quantum device physics and quantum information science;
• Understanding of noise models, error mechanisms, and fault-tolerant quantum systems across one or more qubit modalities.
• Broad understanding of quantum control, including pulse-level hardware interfaces and classical feedback through software abstractions.
• Excellent communication and collaboration skills.

Preferred:

• Experience developing learned calibration or decoding models and deploying them in real-time quantum control feedback loops.
• Understanding of latency and throughput constraints in real-time quantum systems.
• Deep expertise in reinforcement learning, including policy optimization, reward shaping, and sim-to-real transfer for physical systems or closed-loop control.
• Experience with physics-informed or generative approaches to synthetic data generation;
• Experience with noise simulation, Hamiltonian learning, or data augmentation for scientific AI models.
• Experience with large-scale model training and fine-tuning, including LoRA, QLoRA, adapters, and domain adaptation.
• Proficiency with CUDA and NVIDIA GPU programming for quantum simulation, AI model training, or real-time inference at scale.

Benefits and Compensation:

• Base salary range of $192,000 to $304,750 USD, depending on location, experience, and comparable employee compensation.
• Eligible for equity and benefits.
• Competitive salary and comprehensive benefits package.
• Benefits available to employees and their families.