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Quantum Computing Research Scientist

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – 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 computing solutions to solve humanity's greatest challenges—from climate modeling to drug discovery. As a Quantum Research Scientist, you'll collaborate with Nobel laureates and disrupt industries by harnessing quantum mechanics to create computational paradigms beyond classical limits. Our state-of-the-art lab in San Francisco offers unparalleled resources to turn theoretical breakthroughs into real-world impact. If you're driven to shape the next technological epoch, this is where your legacy begins.

Responsibilities

  • Design and implement novel quantum algorithms for optimization and simulation problems
  • Lead experimental validation of quantum hardware prototypes in cryogenic environments
  • Develop error-correction frameworks for fault-tolerant quantum systems
  • Collaborate with cross-disciplinary teams to translate quantum solutions into industry applications
  • Publish groundbreaking research in top-tier journals and conferences
  • Secure federal and private funding for quantum computing initiatives
  • Mentor junior researchers in quantum information science

Qualifications

  • PhD in Physics, Computer Science, or related field (or equivalent industry experience)
  • Expertise in quantum programming languages (Q#, Quil, or Cirq)
  • Proven track record in quantum algorithm development or quantum error correction
  • Experience with quantum hardware platforms (IBM Quantum, Rigetti, or IonQ)
  • Strong background in linear algebra, probability theory, and computational complexity
  • Publication record in quantum computing or condensed matter physics
  • Ability to thrive in fast-paced, interdisciplinary research environments

Required Skills

Quantum Computing Quantum Algorithms Cryogenics Q# Python Linear Algebra Machine Learning Fault-Tolerance

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