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Quantum AI Research Scientist

Nexus Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join Nexus Dynamics at the forefront of technological evolution as we pioneer the next generation of quantum-AI hybrid systems. We're seeking visionary Quantum AI Research Scientists to develop revolutionary algorithms that will redefine computational boundaries by 2026. Our multidisciplinary team operates at the intersection of quantum mechanics, machine learning, and advanced cryptography in a state-of-the-art facility with access to IBM Quantum and D-Wave systems.

As a key architect of our 2026 roadmap, you'll collaborate with Nobel laureates and industry pioneers to solve previously unsolvable problems in materials science, drug discovery, and climate modeling. We offer unparalleled resources including dedicated quantum annealing time, custom-designed cryogenic processors, and an annual research budget exceeding $500K per project.

Responsibilities

  • Design and implement novel quantum machine learning algorithms leveraging 200+ qubit processors
  • Lead cross-functional R&D projects targeting exponential speedups in optimization problems
  • Develop error-corrected quantum neural networks for real-time pattern recognition
  • Collaborate with hardware teams to co-design quantum-AI accelerator architectures
  • Publish breakthrough research in Nature/Science and contribute to open-source quantum frameworks
  • Secure $1M+ in government/industry grants for quantum-AI initiatives by 2026
  • Mentor PhD candidates in quantum computing while establishing new industry benchmarks

Qualifications

  • PhD in Quantum Computing, Physics, or Computational Mathematics (exceptional MS candidates with 5+ years experience considered)
  • Published research in quantum algorithms or quantum machine learning (arXiv/IEEE journals)
  • Proficiency with Qiskit, Cirq, or PennyLane frameworks and quantum circuit optimization
  • Expertise in tensor networks and quantum error correction protocols (surface codes, LDPC)
  • Experience with hybrid quantum-classical computing architectures (QAOA, VQE)
  • Deep understanding of quantum annealing and adiabatic quantum computing principles
  • Proven ability to translate theoretical quantum models into practical implementations

Required Skills

Quantum Computing Machine Learning Qiskit Cirq Quantum Error Correction Tensor Networks QAOA VQE Adiabatic Quantum Computing Python C++ Research

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