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Quantum AI Integration Specialist

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
17 Mei 2026
Deadline
17 Mei 2027

Job Description

Join Nexus Future Labs at the forefront of technological evolution in 2026. We're pioneering the convergence of quantum computing and artificial intelligence to solve humanity's most complex challenges. As a Quantum AI Integration Specialist, you'll architect the next generation of hybrid quantum-classical systems that redefine computational boundaries. Our collaborative environment fosters groundbreaking research with industry-leading resources and a team of Nobel-caliber scientists. If you're passionate about shaping the future of technology, this is your calling.

Responsibilities

  • Design and deploy hybrid quantum-AI frameworks for real-world applications
  • Optimize quantum algorithms for machine learning model acceleration
  • Develop error-correction protocols for large-scale quantum neural networks
  • Collaborate with hardware teams to co-design quantum-AI accelerator chips
  • Lead cross-functional projects integrating quantum computing with autonomous systems
  • Publish breakthrough research in top-tier quantum computing journals
  • Secure federal and private research grants for quantum-AI initiatives

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (or equivalent experience)
  • 5+ years in quantum algorithm development or quantum machine learning
  • Expertise in quantum programming languages (Qiskit, Cirq, Q#)
  • Proven track record of publishing in Nature/Science journals
  • Proficiency in Python, TensorFlow, and high-performance computing frameworks
  • Experience with quantum hardware platforms (IBM Quantum, Rigetti, D-Wave)
  • Strong background in tensor networks and quantum information theory
  • Ability to translate complex quantum concepts into business applications

Required Skills

Quantum Computing Quantum Machine Learning Qiskit Cirq TensorFlow High-Performance Computing Quantum Error Correction Tensor Networks Algorithm Design Research Leadership

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