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Quantum AI Research Scientist

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

Job Description

Join Nexus Quantum Dynamics at the forefront of 2026's technological revolution! We're pioneering the convergence of quantum computing and artificial intelligence to solve humanity's greatest challenges. As a Quantum AI Research Scientist, you'll architect next-gen algorithms and collaborate with Nobel laureates in our state-of-the-art Austin lab. Your work will directly impact breakthroughs in drug discovery, climate modeling, and autonomous systems. We offer competitive equity packages, unlimited learning stipends, and a culture where your radical ideas become reality. Shape the future with us.

Responsibilities

  • Design and implement novel quantum machine learning algorithms for 2026-era computational challenges
  • Lead cross-functional R&D teams in developing quantum-optimized neural networks
  • Author breakthrough research papers for Nature/Science journals and premier AI conferences
  • Translate theoretical quantum models into scalable production-grade systems
  • Collaborate with hardware teams to co-design quantum-AI hybrid architectures
  • Secure $5M+ in federal and industry research grants for frontier projects

Qualifications

  • PhD in Quantum Computing, Machine Learning, or Computational Physics (or equivalent experience)
  • 3+ years of hands-on experience with quantum programming (Qiskit, Cirq, or PennyLane)
  • Published research in quantum machine learning or top-tier AI conferences (NeurIPS, ICML)
  • Expertise in Python, TensorFlow/PyTorch, and high-performance computing frameworks
  • Deep understanding of quantum error correction and fault-tolerant systems
  • Proven track record of leading complex R&D projects from concept to deployment
  • Strong background in physics, mathematics, and algorithmic optimization

Required Skills

Quantum Computing Machine Learning Python TensorFlow Qiskit Research Neural Networks High-Performance Computing Physics

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