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Quantum AI Research Scientist

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 200.000 – USD 280.000
New
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Join Nexus Quantum Labs at the forefront of 2026's technological revolution. We're pioneering quantum-AI fusion systems that will redefine computational boundaries. As a Quantum AI Research Scientist, you'll architect breakthrough algorithms for our next-gen quantum neural networks, working in our state-of-the-art lab in San Francisco. Collaborate with Nobel laureates and industry disruptors to solve humanity's grandest challenges—from climate modeling to personalized medicine.

We offer competitive equity packages, unlimited R&D budgets, and flexible work arrangements. Your innovations will directly shape the quantum-AI landscape for decades to come.

Responsibilities

  • Design and implement quantum machine learning algorithms for 128-qubit+ processors
  • Lead cross-functional teams in developing hybrid quantum-classical AI frameworks
  • Author peer-reviewed publications in top-tier journals (Nature, Science, etc.)
  • Optimize quantum neural networks for real-world enterprise applications
  • Develop error-correction protocols for fault-tolerant quantum AI systems
  • Secure $5M+ in government/private research funding
  • Mentor PhD candidates in quantum-AI specialization

Qualifications

  • PhD in Quantum Computing, Machine Learning, or Physics (or equivalent experience)
  • 3+ years hands-on quantum algorithm development on platforms like IBM Q or Rigetti
  • Publication record in quantum machine learning or related fields
  • Expertise in TensorFlow Quantum, PennyLane, or similar quantum-AI frameworks
  • Strong Python/C++ proficiency with low-level quantum hardware integration experience
  • Understanding of quantum error correction codes (surface codes, LDPC codes)
  • Experience securing government SBIR grants or DARPA contracts

Required Skills

Quantum Computing Machine Learning TensorFlow Quantum Python C++ Quantum Error Correction Research Funding Peer Review

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