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2026 Quantum AI Research Lead

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

Job Description

Join Nexus Dynamics at the forefront of technological evolution as we pioneer the next generation of quantum-AI integration. As the 2026 Quantum AI Research Lead, you'll architect breakthrough solutions that redefine computational boundaries in our Austin innovation hub. Collaborate with Nobel laureates and disrupt industries through cutting-edge research in quantum supremacy, neural networks, and predictive analytics. We offer unparalleled resources, flexible work arrangements, and equity in a company valued at $2B+. Shape the future of humanity's most transformative technologies.

Responsibilities

  • Lead cross-functional R&D teams in developing quantum-AI hybrid systems for real-world applications
  • Architect scalable algorithms leveraging quantum computing and deep learning frameworks
  • Secure $5M+ annual research grants from NSF, DARPA, and private investors
  • Publish breakthrough findings in Nature/Science journals and present at global conferences
  • Mentor PhD-level researchers and establish industry partnerships with quantum hardware pioneers
  • Drive ethical AI governance frameworks for quantum deployment in critical infrastructure

Qualifications

  • PhD in Quantum Computing, Machine Learning, or Computational Physics with 5+ years industry experience
  • Proven track record of publishing in top-tier journals and securing federal research funding
  • Expertise in quantum algorithms (QAOA, VQE) and deep learning architectures (Transformers, GANs)
  • Experience with quantum platforms (IBM Q, Rigetti, D-Wave) and cloud quantum services
  • Strong background in high-performance computing (HPC) and parallel processing frameworks
  • Exceptional leadership in managing R&D teams with $10M+ budgets
  • Knowledge of NIST quantum security standards and ethical AI frameworks

Required Skills

Quantum Computing Machine Learning AI Ethics High-Performance Computing Research Leadership Federal Grants Algorithm Design

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