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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Computing Architect

Nexus Quantum Dynamics
San Francisco
Estimated Salary
USD 220.000 – USD 320.000
Live Update
21 Mei 2026
Deadline
21 Mei 2027

Job Description

Join Nexus Quantum Dynamics as a Quantum Computing Architect to pioneer the next technological revolution. We're building the quantum infrastructure that will define 2026 and beyond. You'll design scalable quantum systems, optimize algorithms for real-world applications, and collaborate with Nobel laureates to solve humanity's most complex challenges.

This role offers unparalleled opportunities to shape quantum-resistant cryptography, accelerate drug discovery, and revolutionize AI. Our state-of-the-art lab features 256-qubit processors and exclusive partnerships with NASA and MIT. We offer equity, flexible remote options, and a $50K annual learning stipend.

Responsibilities

  • Architect quantum algorithms for optimization, simulation, and machine learning applications
  • Design error-corrected quantum circuits and hybrid classical-quantum systems
  • Lead quantum hardware integration projects with superconducting and photonic platforms
  • Develop quantum-resistant cryptographic protocols for financial and defense sectors
  • Mentor junior quantum engineers and publish research in Nature/Science journals
  • Collaborate with C-suite to roadmap 5-year quantum adoption strategy

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field (MS with 5+ years experience)
  • Expertise in Qiskit, Cirq, or quantum circuit design frameworks
  • Proven experience with quantum error correction and fault tolerance
  • Strong background in linear algebra, complex analysis, and information theory
  • Published research in quantum computing or quantum information
  • Experience with AWS Braket or Azure Quantum platforms
  • Security clearance preferred for government projects

Required Skills

Quantum Computing Qiskit Cirq Quantum Algorithms Error Correction Cryptography Linear Algebra Machine Learning

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