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Quantum Infrastructure Lead 2026

Nexus Quantum Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
19 Mei 2026
Deadline
19 Mei 2027

Job Description

Join Nexus Quantum Dynamics at the forefront of technological evolution as we pioneer the next generation of quantum computing infrastructure. We're seeking a visionary Quantum Infrastructure Lead to architect, deploy, and optimize quantum systems that will redefine computational capabilities by 2026. This role demands a blend of deep technical expertise and strategic foresight to solve humanity's most complex challenges.

At Nexus Quantum Dynamics, you'll collaborate with Nobel laureates and industry pioneers in our state-of-the-art San Francisco lab. We offer competitive equity packages, flexible work arrangements, and unparalleled opportunities to shape the quantum revolution.

Responsibilities

  • Design and deploy scalable quantum computing architectures with 1000+ qubit capacity
  • Lead cross-functional teams of physicists, engineers, and data scientists
  • Develop fault-tolerant quantum error correction protocols for 2026 deployment
  • Establish quantum-classical hybrid computing frameworks
  • Drive R&D initiatives in quantum materials and cryogenic systems
  • Secure $50M+ in quantum infrastructure grants and partnerships
  • Present breakthrough findings at IEEE Quantum Week and Nature conferences

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 5+ years experience in quantum hardware or algorithm development
  • Proven track record of leading large-scale R&D projects
  • Expertise in quantum error correction and topological qubits
  • Published research in peer-reviewed quantum computing journals
  • Proficiency with quantum simulation frameworks (Qiskit, Cirq)
  • Experience with cryogenic engineering and superconducting systems
  • Strong background in machine learning for quantum optimization

Required Skills

Quantum Computing Cryogenic Engineering Topological Qubits Quantum Error Correction Machine Learning Superconducting Systems R&D Leadership Scientific Publishing

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