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Quantum Computing Research Scientist

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
Live Update
3 Juli 2026
Deadline
3 Jul 2027

Job Description

Join Nexus Quantum Labs at the forefront of 2026's technological revolution. We're pioneering quantum computing breakthroughs that will redefine industries worldwide. As a Quantum Computing Research Scientist, you'll collaborate with Nobel laureates and MIT alumni to develop scalable quantum algorithms and error-corrected systems. Our state-of-the-art facility in downtown San Francisco offers unparalleled resources, including access to IBM Quantum and D-Wave systems. We offer competitive equity packages, flexible hybrid work arrangements, and dedicated R&D budgets for cutting-edge experiments.

Responsibilities

  • Design and implement quantum algorithms for optimization problems in logistics, cryptography, and drug discovery
  • Develop novel error-correction protocols to overcome quantum decoherence challenges
  • Collaborate with hardware teams to calibrate and test next-generation quantum processors
  • Publish research in Nature Physics and IEEE Quantum journals
  • Lead open-source quantum software initiatives on GitHub
  • Mentor PhD interns from Stanford and Berkeley quantum programs
  • Secure $1M+ in NSF and DoE quantum computing grants

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field (postdoc preferred)
  • 5+ years of experience with quantum programming languages (Q#, Qiskit, Cirq)
  • Published research in top-tier quantum computing conferences
  • Expertise in quantum error correction and fault-tolerant architectures
  • Strong background in linear algebra, probability theory, and complexity classes
  • Experience with quantum hardware platforms (Rigetti, IonQ, Honeywell)
  • Proficiency in Python, C++, and high-performance computing frameworks

Required Skills

Quantum Computing Qiskit Q# Quantum Algorithms Error Correction Linear Algebra Python C++ Fault-Tolerant Systems Quantum Hardware Machine Learning

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