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2026 Quantum AI Researcher

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer solutions for 2026 and beyond. We're seeking visionary Quantum AI Researchers to develop groundbreaking algorithms that will redefine computational boundaries. Our state-of-the-art facility in downtown San Francisco offers unparalleled resources for collaborative innovation, with competitive benefits including equity packages and flexible R&D time. This role represents a unique opportunity to shape humanity's technological trajectory while working alongside Nobel laureates and industry pioneers.

Responsibilities

  • Design and implement quantum machine learning algorithms for next-gen AI systems
  • Lead cross-functional teams in developing fault-tolerant quantum computing architectures
  • Author peer-reviewed publications on quantum-AI hybrid systems for 2026 applications
  • Collaborate with hardware teams to optimize quantum-AI integration protocols
  • Develop predictive models for quantum algorithm scalability beyond current limitations
  • Mentor junior researchers in quantum computing best practices
  • Secure $5M+ in research grants for quantum-AI initiatives

Qualifications

  • PhD in Quantum Computing, Physics, or Computational Mathematics
  • 5+ years experience in quantum algorithm development or quantum machine learning
  • Published research in Nature/Science journals on quantum systems
  • Proficiency in Qiskit, Cirq, and quantum circuit optimization frameworks
  • Deep understanding of quantum error correction and fault tolerance
  • Experience with high-performance computing clusters (HPC)
  • Demonstrated ability to translate theoretical models into practical implementations
  • Strong background in AI/ML with TensorFlow or PyTorch expertise

Required Skills

Quantum Computing Machine Learning Qiskit Cirq AI/ML Quantum Error Correction High-Performance Computing TensorFlow PyTorch

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