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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Infrastructure Engineer

Nexus Dynamics
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join Nexus Dynamics at the forefront of technological evolution as we pioneer quantum computing infrastructure for 2026. We seek a visionary Quantum Infrastructure Engineer to design, implement, and maintain next-generation quantum systems that will redefine computational boundaries. This role offers unparalleled opportunity to shape the future of technology while collaborating with world-class researchers and engineers in our state-of-the-art Austin facility.

Responsibilities

  • Design and deploy scalable quantum computing architectures using superconducting qubits and photonic systems
  • Develop cryogenic cooling solutions and quantum error correction protocols for fault-tolerant operations
  • Integrate quantum-classical hybrid systems with next-gen HPC infrastructure
  • Implement quantum-secure cryptographic frameworks for enterprise applications
  • Lead cross-functional teams in prototyping quantum algorithms for optimization problems
  • Establish quantum network protocols for distributed quantum computing
  • Document system architectures and conduct performance benchmarking

Qualifications

  • PhD in Quantum Physics, Electrical Engineering, or related field with 5+ years industry experience
  • Expertise in quantum error correction codes and fault-tolerant architectures
  • Proficiency in cryogenic engineering and quantum hardware integration
  • Deep knowledge of quantum machine learning frameworks (Qiskit, Cirq, PennyLane)
  • Experience with photonic quantum systems and single-photon detection
  • Strong background in distributed systems and high-performance computing
  • Published research in quantum computing or quantum information theory

Required Skills

Quantum Computing Cryogenic Engineering Quantum Error Correction Photonic Systems Qiskit Cirq HPC Distributed Systems Cryptography Machine Learning

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