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Quantum Computing Architect

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer the next computing paradigm. We're seeking a visionary Quantum Computing Architect to design scalable quantum systems that will redefine industries by 2026. This role offers the unique opportunity to shape humanity's technological future while working alongside Nobel laureates and quantum pioneers in our state-of-the-art San Francisco facility.

Our team operates at the intersection of physics, computer science, and engineering to solve previously unsolvable problems. You'll lead the development of fault-tolerant quantum processors and hybrid quantum-classical algorithms that will accelerate breakthroughs in drug discovery, materials science, and artificial intelligence.

Responsibilities

  • Design and implement quantum circuit architectures for 1000+ qubit systems
  • Develop quantum error correction protocols and fault-tolerance frameworks
  • Create hybrid quantum-classical workflows for enterprise applications
  • Lead cross-disciplinary R&D teams in quantum algorithm optimization
  • Establish industry standards for quantum system interoperability
  • Drive patent development in quantum hardware-software co-design
  • Collaborate with Fortune 500 partners on quantum adoption roadmaps

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 5+ years experience in quantum computing architecture
  • Expertise in superconducting qubit systems and cryogenic engineering
  • Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
  • Published research in top-tier quantum computing journals
  • Experience with quantum machine learning frameworks
  • Strong background in distributed computing and high-performance systems

Required Skills

Quantum Computing Qubit Design Error Correction Quantum Algorithms Cryogenic Engineering Q# Machine Learning Superconducting Systems

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