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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Computing Architect

Nexus Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
12 Mei 2026
Deadline
12 Mei 2027

Job Description

Join Nexus Dynamics at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking a visionary Quantum Computing Architect to design next-gen quantum systems that will redefine computational boundaries. This role offers unparalleled opportunities to shape the future of artificial intelligence, cryptography, and complex system simulations.

Why Nexus Dynamics? We're a Silicon Valley trailblack combining cutting-edge research with scalable commercial applications. Our team collaborates with MIT, Caltech, and NASA to develop quantum systems that solve previously unsolvable problems. Enjoy flexible work arrangements, equity packages, and access to our state-of-the-art quantum lab.

Responsibilities

  • Design scalable quantum computing architectures for enterprise applications
  • Develop quantum algorithms optimizing machine learning models
  • Lead cross-functional teams of physicists and software engineers
  • Integrate quantum systems with classical computing infrastructure
  • Research and implement error correction protocols for quantum stability
  • Collaborate with government agencies on quantum security frameworks
  • Publish findings in leading journals and industry conferences

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 5+ years experience in quantum algorithm development
  • Proficiency in quantum programming languages (Q#, Qiskit)
  • Expertise in quantum error correction techniques
  • Strong background in distributed systems and cloud architecture
  • Published research in quantum computing or theoretical physics
  • Experience with cryogenic engineering and quantum hardware integration

Required Skills

Quantum Computing Quantum Algorithms Q# Qiskit Cryogenic Engineering Distributed Systems Machine Learning Cryptography

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