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Quantum Computing Architect

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
11 Mei 2026
Deadline
11 Mei 2027

Job Description

Join Nexus Labs at the forefront of 2026's quantum revolution. We're seeking a pioneering Quantum Computing Architect to design next-generation systems that will redefine computational boundaries. In this role, you'll spearhead the development of fault-tolerant quantum processors while collaborating with Nobel laureates and MIT-affiliated researchers. Our Austin campus features state-of-the-art cryogenic labs and direct access to quantum cloud infrastructure.

This position offers unparalleled opportunities to shape the quantum landscape through patent development, open-source contributions, and cross-industry partnerships with aerospace, pharmaceutical, and climate modeling leaders. You'll receive equity grants and annual research stipends to pursue breakthrough innovations.

Responsibilities

  • Design scalable quantum algorithms for practical commercial applications
  • Architect hybrid quantum-classical computing frameworks
  • Lead fault-tolerance engineering for 100+ qubit systems
  • Collaborate with hardware teams on quantum error correction protocols
  • Develop quantum security frameworks for post-quantum cryptography
  • Present quarterly breakthroughs to our Quantum Advisory Board
  • Mentor junior researchers in quantum machine learning

Qualifications

  • PhD in Quantum Physics/Computer Science or equivalent experience
  • 3+ years building quantum algorithms on real hardware
  • Published research in Nature/Science or IEEE journals
  • Expertise in Qiskit, Cirq, or Q# frameworks
  • Deep understanding of quantum decoherence mitigation
  • Experience with superconducting qubit systems
  • Strong background in topological quantum computing
  • Certification in quantum machine learning from MIT

Required Skills

Quantum Computing Qiskit Fault-Tolerance Quantum Algorithms Cryogenic Engineering Post-Quantum Cryptography Topological Quantum Computing Machine Learning

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