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Quantum AI Research Lead

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

Job Description

Join Nexus Labs at the forefront of technological revolution as we pioneer quantum-AI convergence for 2026. We're seeking a visionary Quantum AI Research Lead to architect next-generation systems that will redefine computational boundaries. This role offers unparalleled opportunity to shape humanity's technological future through cutting-edge research in quantum machine learning, neural-symbolic integration, and post-classical computing paradigms.

Our state-of-the-art facility in San Francisco provides an ecosystem where Nobel-caliber research meets industry application. You'll collaborate with world-class physicists, computer scientists, and engineers to develop quantum neural networks, hybrid quantum-classical algorithms, and fault-tolerant computing architectures. We offer competitive compensation, flexible work arrangements, and comprehensive benefits including equity participation in our groundbreaking ventures.

Responsibilities

  • Lead research in quantum machine learning algorithms and neural-symbolic AI systems
  • Design and implement hybrid quantum-classical computing architectures for 2026 applications
  • Collaborate with hardware teams to develop quantum error correction protocols and fault-tolerant systems
  • Publish breakthrough research in top-tier journals and industry whitepapers
  • Mentor cross-functional teams of quantum physicists, AI engineers, and domain specialists
  • Develop strategic roadmaps for quantum-AI integration in healthcare, logistics, and materials science
  • Secure patents and intellectual property for novel quantum computational methods

Qualifications

  • PhD in Quantum Computing, Machine Learning, or Physics with 5+ years industry experience
  • Expertise in quantum programming languages (Qiskit, Cirq, Q#) and quantum circuit design
  • Published research in quantum machine learning or neural-symbolic integration
  • Proficiency in high-performance computing frameworks (PyTorch, TensorFlow, JAX)
  • Demonstrated experience with quantum hardware platforms (IBM Quantum, Rigetti, IonQ)
  • Strong background in topological quantum computing and quantum error correction
  • Ability to translate complex quantum concepts into practical business applications

Required Skills

Quantum Computing Machine Learning Neural Networks Quantum Algorithms Python C++ Qiskit Fault-Tolerant Computing AI Research

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