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Quantum Computing Architect

Nexus Quantum Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
30 Juni 2026
Deadline
30 Jun 2027

Job Description

Join the vanguard of technological revolution at Nexus Quantum Labs, where we're engineering the computational frontier for 2026 and beyond. We seek a visionary Quantum Computing Architect to design next-generation quantum systems that will redefine industries. You'll lead groundbreaking research in quantum algorithms, error correction, and scalable architectures while collaborating with Nobel laureates and industry pioneers. Our Austin hub offers a dynamic environment where your innovations will directly impact global tech infrastructure. This role includes competitive equity, unlimited learning stipends, and the opportunity to shape humanity's computational future.

Responsibilities

  • Design scalable quantum computing architectures for 100+ qubit systems
  • Develop quantum error correction protocols achieving >99.9% fidelity
  • Create hybrid quantum-classical algorithms for optimization problems
  • Lead cross-functional teams of physicists and software engineers
  • File 3+ patents annually in quantum computing innovations
  • Collaborate with government agencies on quantum security standards
  • Present breakthroughs at IEEE Quantum Computing Summit

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 5+ years experience in quantum algorithm development
  • Expertise in superconducting qubit systems or topological quantum computing
  • Published research in Nature/Science journals on quantum systems
  • Proficiency in Qiskit, Cirq, and quantum circuit optimization
  • Strong background in cryogenic engineering and quantum control systems
  • Experience with quantum machine learning frameworks
  • Security clearance preferred for government projects

Required Skills

Quantum Computing Quantum Algorithms Cryogenic Engineering Superconducting Qubits Qiskit Quantum Error Correction Topological Quantum Computing Machine Learning

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