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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Computing Architect

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

Job Description

Join Nexus Quantum Solutions at the forefront of technological revolution as we pioneer quantum computing applications for 2026 and beyond. We seek a visionary Quantum Computing Architect to design next-gen quantum systems that will redefine industries. This role offers unparalleled opportunity to shape the future of computation while working with world-class researchers and cutting-edge hardware.

Our Austin-based innovation hub provides dynamic collaboration spaces, state-of-the-art quantum labs, and competitive benefits including equity participation. You'll lead projects with global impact while enjoying Texas' thriving tech ecosystem.

Responsibilities

  • Design scalable quantum algorithms for practical commercial applications
  • Architect hybrid quantum-classical computing frameworks
  • Develop error correction protocols for fault-tolerant quantum systems
  • Lead quantum software integration with existing enterprise infrastructures
  • Collaborate with hardware teams to optimize quantum processor performance
  • Research emerging quantum technologies and their market viability
  • Present quantum solutions to C-level executives and technical stakeholders

Qualifications

  • PhD in Physics, Computer Science, or related field (MS with 5+ years experience)
  • Expertise in quantum programming languages (Qiskit, Cirq, Quipper)
  • Proven track record in quantum algorithm development
  • Deep understanding of quantum error correction and decoherence
  • Experience with cloud quantum platforms (IBM Quantum, Amazon Braket)
  • Strong background in high-performance computing architectures
  • Published research in peer-reviewed quantum computing journals

Required Skills

Quantum Computing Quantum Algorithms Qiskit Cirq Quantum Error Correction Hybrid Computing High-Performance Computing Quantum Machine Learning

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