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2026 Quantum AI Architect

Nexus Future Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
14 Mei 2026
Deadline
14 Mei 2027

Job Description

Join Nexus Future Labs at the forefront of technological evolution. We're pioneering the next generation of quantum-AI convergence systems that will redefine computing by 2026. As a Quantum AI Architect, you'll design revolutionary neural-quantum hybrid architectures and lead cross-disciplinary teams to solve humanity's most complex challenges. Our culture thrives on audacious innovation, collaborative disruption, and relentless curiosity.

Why Nexus? We offer cutting-edge resources, unlimited learning stipends, and equity in a company valued at $2B+. Work alongside Nobel laureates and Turing Award winners while shaping the technological landscape of tomorrow.

Responsibilities

  • Design and implement quantum-AI hybrid neural networks for next-gen computing systems
  • Lead R&D initiatives in quantum machine learning algorithms and error correction protocols
  • Collaborate with quantum hardware teams to optimize AI-quantum interface architectures
  • Develop scalable frameworks for quantum-resistant AI security protocols
  • Present breakthrough research at global tech summits and peer-reviewed journals
  • Mentor junior scientists and foster cross-functional innovation across 15+ labs

Qualifications

  • PhD in Quantum Computing, AI, or Computational Physics (or equivalent experience)
  • 5+ years developing quantum algorithms or AI neural networks at scale
  • Expertise in quantum error correction and fault-tolerant architectures
  • Published research in Nature/Science or top-tier ML conferences
  • Proficiency with Qiskit, TensorFlow Quantum, and quantum simulation frameworks
  • Experience leading $1M+ R&D projects with measurable impact
  • Deep understanding of quantum-classical hybrid system integration

Required Skills

Quantum Computing Machine Learning Neural Networks Qiskit TensorFlow Quantum Quantum Error Correction AI Security Research Leadership Computational Physics

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