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Quantum AI Research Scientist

Nexus Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
New
Live Update
1 Juli 2026
Deadline
1 Jul 2027

Job Description

Join Nexus Dynamics at the forefront of technological revolution as we pioneer the next generation of quantum-AI hybrid systems. We're seeking visionary Quantum AI Research Scientists to develop breakthrough algorithms that will redefine computing capabilities by 2026. Our state-of-the-art lab in San Francisco offers unparalleled resources to transform theoretical models into real-world applications across healthcare, climate modeling, and cryptography. You'll collaborate with Nobel laureates and industry disruptors in an environment where innovation is celebrated and failure is reframed as discovery.

Responsibilities

  • Design and implement novel quantum machine learning algorithms for complex optimization problems
  • Lead cross-functional teams in developing error-corrected quantum computing architectures
  • Translate theoretical quantum physics models into scalable AI frameworks
  • Publish breakthrough research in top-tier journals and industry whitepapers
  • Secure and manage federal R&D grants for quantum-AI initiatives
  • Mentor PhD candidates and postdoctoral researchers in quantum computational theory
  • Partner with Fortune 500 clients to implement quantum solutions for enterprise challenges

Qualifications

  • PhD in Quantum Computing, Theoretical Physics, or Computer Science with 3+ years post-grad research
  • Published work in Nature/Science or top-tier quantum computing conferences
  • Expertise in quantum error correction protocols and fault-tolerant architectures
  • Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
  • Strong background in machine learning frameworks (PyTorch, TensorFlow) with quantum extensions
  • Experience securing NSF or DOE grants for quantum research
  • Demonstrated ability to translate complex quantum concepts into commercial applications

Required Skills

Quantum Computing Machine Learning Qiskit Q# Theoretical Physics Fault-Tolerant Systems Research Methodology Python C++ High-Performance Computing

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