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2026 Quantum AI Research Lead

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
18 Mei 2026
Deadline
18 Mei 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer the next wave of quantum AI integration. As the 2026 Quantum AI Research Lead, you'll architect breakthrough systems that redefine computational boundaries and shape humanity's digital future. This role offers unparalleled opportunity to lead cross-disciplinary teams in developing scalable quantum algorithms and neural interfaces for next-generation autonomous systems.

We seek visionary researchers who thrive at the intersection of theoretical physics and machine learning. You'll collaborate with Nobel laureates and Silicon Valley pioneers to implement quantum-resistant cryptography and deploy AI models operating at near-quantum computational efficiency. The position includes competitive equity, flexible research budgets, and access to our state-of-the-art quantum computing infrastructure.

Responsibilities

  • Design and implement scalable quantum neural networks for 2026-era autonomous systems
  • Lead cross-functional teams in developing quantum-resistant cryptographic protocols
  • Author breakthrough research papers for Nature Physics and IEEE journals
  • Architect hybrid quantum-classical computing frameworks for enterprise clients
  • Mentor PhD researchers in quantum machine learning methodologies
  • Secure $5M+ annual research grants from DARPA and NSF
  • Present findings at World Economic Forum and MIT Technology Review Summits

Qualifications

  • PhD in Quantum Computing, Theoretical Physics, or Computational Mathematics
  • 5+ years leading quantum AI research with peer-reviewed publications
  • Expertise in Qiskit, Cirq, and quantum error correction algorithms
  • Proven track record of securing federal research grants
  • Proficiency in tensor networks and quantum variational circuits
  • Experience with neuromorphic computing architectures
  • Deep understanding of quantum entanglement in machine learning contexts
  • Published work in top-tier quantum computing conferences

Required Skills

Quantum Computing Machine Learning Theoretical Physics Cryptography Neural Networks Qiskit Cirq Tensor Networks Neuromorphic Computing DARPA Grants

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