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Quantum AI Architect - 2026 Vision

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
New
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join Nexus Future Labs at the forefront of technological evolution as we pioneer the next wave of quantum-AI integration for 2026. We're seeking a visionary Quantum AI Architect to design and implement groundbreaking solutions that will redefine industries. This role offers unparalleled opportunities to shape the future of computational intelligence while working in our state-of-the-art San Francisco facility.

As a key member of our innovation team, you'll collaborate with Nobel laureates and industry pioneers to develop scalable quantum systems powered by advanced machine learning algorithms. We offer competitive compensation, equity packages, and a culture that celebrates bold thinking.

Responsibilities

  • Architect quantum-AI hybrid systems for enterprise-scale applications
  • Lead R&D initiatives in quantum machine learning algorithms
  • Develop roadmaps for quantum computing integration with classical AI frameworks
  • Mentor cross-functional teams in quantum principles and AI optimization
  • Secure patents for novel quantum-AI methodologies
  • Collaborate with hardware teams to co-design quantum processors
  • Publish research in top-tier journals and industry forums

Qualifications

  • PhD in Quantum Computing, AI, or related field (MS with 8+ years experience)
  • Proven expertise in quantum algorithms and error correction
  • Deep knowledge of transformer architectures and neural networks
  • Experience with quantum programming languages (Qiskit, Cirq, etc.)
  • Published research in quantum machine learning or quantum AI
  • Strong background in high-performance computing environments
  • Track record of leading technical teams in emerging tech domains

Required Skills

Quantum Computing Machine Learning AI Architecture Quantum Algorithms High-Performance Computing Research & Development Technical Leadership Quantum Machine Learning

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