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Quantum AI Research Scientist

Nexus Labs
Austin
Estimated Salary
USD 180.000 – USD 280.000
Live Update
25 Mei 2026
Deadline
25 Mei 2027

Job Description

Join Nexus Labs at the frontier of technological evolution. We're pioneering quantum-AI hybrid systems that will redefine computing by 2026. As a Quantum AI Research Scientist, you'll architect next-gen algorithms that merge quantum mechanics with neural networks, solving previously unsolvable problems in cryptography, drug discovery, and climate modeling. Our Austin campus features state-of-the-art quantum labs and collaborative spaces where your innovations could shape humanity's digital future.

We offer competitive compensation, equity packages, and a culture that values intellectual curiosity. Your work will directly impact breakthroughs in autonomous systems, sustainable energy, and personalized medicine.

Responsibilities

  • Design and implement quantum machine learning algorithms for real-world applications
  • Lead cross-functional teams in developing quantum-AI hybrid computing frameworks
  • Research error correction techniques for fault-tolerant quantum systems
  • Collaborate with hardware teams to optimize quantum hardware-software interfaces
  • Publish findings in peer-reviewed journals and present at international conferences
  • Secure research grants and partnerships with academic institutions
  • Develop patents for novel quantum-AI methodologies

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (MS with exceptional experience considered)
  • 3+ years of hands-on quantum algorithm development experience
  • Proficiency in quantum programming languages (Q#, Qiskit, or Cirq)
  • Deep understanding of quantum error correction and fault tolerance
  • Strong background in machine learning frameworks (TensorFlow/PyTorch)
  • Published research in quantum computing or quantum machine learning
  • Experience with high-performance computing environments

Required Skills

Quantum Computing Machine Learning Q# Qiskit TensorFlow Algorithm Design Fault Tolerance Research

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