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Quantum AI Engineer 2026

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

Job Description

Join Nexus Dynamics at the forefront of technological evolution as we pioneer quantum-AI integration for 2026 and beyond. We're seeking visionary Quantum AI Engineers to architect next-gen solutions that redefine computational boundaries. You'll collaborate with Nobel laureates in our Austin R&D hub, developing hybrid quantum-classical systems that solve previously unsolvable challenges in medicine, climate modeling, and cryptography.

Our culture celebrates intellectual audacity – here, impossible problems are invitations to innovate. Enjoy cutting-edge resources including our 256-qubit quantum processor lab, flexible hybrid work arrangements, and dedicated innovation time for personal research initiatives.

Responsibilities

  • Design and implement quantum algorithms optimized for real-world AI applications
  • Develop error-correction protocols for quantum-neural network hybrids
  • Create simulation frameworks for quantum machine learning models
  • Lead cross-functional teams to deploy quantum-AI solutions in production
  • Research novel quantum computing paradigms for 2026+ applications
  • Collaborate with hardware teams to co-design quantum processors
  • Author white papers and contribute to open-source quantum-AI frameworks

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (MS with exceptional experience)
  • Proficiency in quantum programming languages (Qiskit, Cirq, Q#)
  • Expertise in machine learning frameworks (PyTorch, TensorFlow)
  • Published research in quantum machine learning or quantum algorithms
  • Experience with superconducting quantum processors or trapped-ion systems
  • Strong background in complex mathematical modeling
  • Demonstrated ability to translate theoretical concepts into practical implementations

Required Skills

Quantum Computing Machine Learning Python Qiskit TensorFlow Quantum Algorithms Error Correction AI Research

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