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Quantum Computing Architect

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

Job Description

Join Nexus Quantum Labs at the forefront of computational revolution! We're pioneering the next generation of quantum systems that will redefine 2026's technological landscape. As a Quantum Computing Architect, you'll design fault-tolerant quantum processors, optimize quantum algorithms for real-world applications, and collaborate with Nobel laureates to solve humanity's grandest challenges. Our state-of-the-art lab offers cutting-edge resources and an unparalleled innovation ecosystem.

Why Nexus Quantum Labs? We're not just building computers – we're creating the computational framework for 2026's breakthroughs in medicine, climate modeling, and artificial intelligence. Your work will directly impact the future.

Responsibilities

  • Design scalable quantum processor architectures with >1000 qubit coherence
  • Develop hybrid quantum-classical algorithms for industrial optimization problems
  • Lead fault-tolerance implementations achieving <10-12 error rates
  • Collaborate with AI teams to integrate quantum acceleration into machine learning pipelines
  • Drive quantum security protocols for post-quantum cryptography standards
  • Present architectural blueprints to C-suite executives and government agencies

Qualifications

  • PhD in Quantum Physics, Computer Science, or Electrical Engineering (MS with 8+ years experience)
  • Expertise in superconducting qubit systems or topological quantum computing
  • Publication record in Nature/Science or equivalent quantum computing journals
  • Proficiency with Qiskit, Cirq, or proprietary quantum simulation frameworks
  • Experience with cryogenic engineering and quantum control systems
  • Demonstrated ability to translate theoretical models into physical implementations

Required Skills

Quantum Computing Superconducting Qubits Quantum Error Correction Cryogenic Engineering Quantum Algorithms Machine Learning Fault-Tolerant Design

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