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Quantum Computing Architect

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Shape the future of technology as our Quantum Computing Architect. Join Nexus Labs at the forefront of quantum innovation, where you'll design next-gen quantum systems that will redefine computational boundaries. This role offers unparalleled opportunity to pioneer breakthroughs in cryptography, AI optimization, and materials science while mentoring the next generation of quantum engineers. Our Austin campus features state-of-the-art quantum labs and collaborative spaces where visionary minds converge.

Responsibilities

  • Design and implement scalable quantum computing architectures using superconducting and photonic systems
  • Develop quantum error correction protocols to achieve fault-tolerant operations
  • Lead cross-functional teams in quantum algorithm optimization for real-world applications
  • Collaborate with research institutions to publish cutting-edge quantum breakthroughs
  • Architect hybrid quantum-classical computing frameworks for enterprise solutions
  • Drive quantum security protocols for next-generation cryptographic systems
  • Mentor junior quantum engineers in emerging technologies like quantum machine learning

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field with 5+ years industry experience
  • Proven expertise in quantum circuit design and quantum algorithm implementation
  • Deep understanding of quantum decoherence mitigation and error correction techniques
  • Experience with quantum programming frameworks (Qiskit, Cirq, Q#)
  • Strong background in high-performance computing and parallel processing
  • Published research in peer-reviewed quantum computing journals
  • Ability to translate complex quantum concepts into actionable business strategies

Required Skills

quantum computing quantum algorithms quantum error correction superconducting qubits quantum cryptography Qiskit Cirq quantum machine learning

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