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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Infrastructure Architect

NeuralCore Systems
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join NeuralCore Systems at the forefront of 2026's technological revolution. We're seeking a visionary Quantum Infrastructure Architect to design and deploy next-generation computing frameworks that will redefine global industries. As a key innovator in our Austin hub, you'll architect hybrid quantum-classical systems, pioneer fault-tolerant architectures, and lead cross-functional teams to solve humanity's most complex challenges. This is your chance to shape the digital landscape of tomorrow.

Responsibilities

  • Design scalable quantum computing infrastructure integrating photonic, superconducting, and topological qubits
  • Develop fault-tolerant quantum error correction protocols for 2026-era systems
  • Lead implementation of quantum-safe cryptographic frameworks
  • Architect hybrid quantum-classical machine learning pipelines
  • Optimize quantum resource allocation for real-time industrial applications
  • Collaborate with quantum hardware teams on cryogenic control system integration
  • Document quantum architecture blueprints using QASM and OpenQASM standards

Qualifications

  • PhD in Quantum Computing, Theoretical Physics, or equivalent practical experience
  • 5+ years designing fault-tolerant quantum architectures
  • Expertise in quantum error correction codes (surface, LDPC, color codes)
  • Proficiency in quantum programming languages (Qiskit, Cirq, Q#)
  • Strong background in distributed systems and high-performance computing
  • Published research in quantum hardware-software co-design
  • Experience with quantum annealing and variational algorithms
  • Security clearance preferred for government-contract projects

Required Skills

Quantum Computing Fault-Tolerant Architecture Quantum Error Correction Qiskit Cirq Q# Cryogenic Systems Distributed Computing Cryptography Machine Learning

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