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Quantum Computing Architect

Nexus Quantum Solutions
Austin
Estimated Salary
USD 180.000 – USD 280.000
New
Live Update
31 Mei 2026
Deadline
31 Mei 2027

Job Description

Join the quantum revolution at Nexus Quantum Solutions as we pioneer the next frontier of computational technology for 2026. We're seeking a visionary Quantum Computing Architect to design and implement scalable quantum systems that will redefine industries. You'll collaborate with Nobel Prize-winning physicists and lead cross-functional teams to develop fault-tolerant quantum processors. Our Austin Innovation Campus offers cutting-edge labs, competitive equity packages, and the opportunity to shape humanity's technological future. If you thrive at the intersection of theoretical physics and practical engineering, this is your moment to make history.

Responsibilities

  • Design and architect fault-tolerant quantum computing systems for commercial applications
  • Lead R&D initiatives in quantum error correction and decoherence mitigation
  • Develop quantum algorithms for optimization, simulation, and machine learning
  • Collaborate with hardware teams to integrate quantum processors with classical systems
  • Secure patents and publish research in top-tier quantum computing journals
  • Mentor quantum engineering talent and build next-generation technical teams

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field (or equivalent experience)
  • 5+ years in quantum computing with demonstrated system architecture expertise
  • Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
  • Deep understanding of quantum error correction and topological qubits
  • Publication record in quantum computing or theoretical physics
  • Experience with cryogenic systems and quantum hardware integration
  • Strong background in linear algebra, quantum mechanics, and information theory

Required Skills

Quantum Computing Quantum Algorithms System Architecture Quantum Error Correction Cryogenic Engineering Qiskit Cirq Q# Machine Learning

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