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Quantum AI Systems Architect

Nexus Quantum Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

Join the forefront of technological evolution as a Quantum AI Systems Architect at Nexus Quantum Dynamics. We're pioneering the next generation of computational intelligence by merging quantum computing with advanced machine learning. This pivotal role demands visionary thinkers who can architect hybrid quantum-classical systems that solve previously unsolvable problems in cryptography, material science, and autonomous systems.

As a key member of our Future Technologies Division, you'll collaborate with Nobel laureates and industry disruptors to develop scalable quantum neural networks. Your work will directly impact breakthroughs in drug discovery, climate modeling, and personalized AI. We offer unparalleled resources, including access to our 128-qubit quantum annealer and dedicated R&D labs.

Responsibilities

  • Design and implement hybrid quantum-classical AI architectures for enterprise-scale applications
  • Develop error-corrected quantum algorithms leveraging Grover's and Shor's optimizations
  • Create adaptive machine learning pipelines that dynamically allocate tasks between classical and quantum processors
  • Lead cross-functional teams in prototyping quantum neural networks for predictive analytics
  • Establish security protocols for quantum-resistant encryption in AI systems
  • Document quantum-computing methodologies and publish findings in peer-reviewed journals
  • Collaborate with hardware teams to optimize qubit coherence times for ML workloads

Qualifications

  • PhD in Quantum Computing, Machine Learning, or related field with 5+ years industry experience
  • Expertise in quantum programming languages (Q#, Qiskit, or Cirq) and quantum circuit design
  • Proven track record of implementing quantum algorithms for real-world applications
  • Deep understanding of quantum error correction and fault-tolerant computing principles
  • Strong background in distributed computing frameworks (TensorFlow, PyTorch) with quantum extensions
  • Published research in quantum machine learning or top-tier conference presentations
  • Experience with cloud quantum platforms (IBM Quantum, Amazon Braket, Microsoft Azure Quantum)
  • Ability to translate complex quantum concepts into actionable engineering requirements

Required Skills

Quantum Computing Machine Learning Q# Qiskit Quantum Algorithms Neural Networks Cryptography Distributed Systems AI Architecture Error Correction

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