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Quantum Computing Research Scientist

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

Job Description

Join Nexus Quantum Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking visionary research scientists to develop breakthrough algorithms and hardware architectures that will redefine computational boundaries. Our state-of-the-art facility in San Francisco offers unparalleled resources and collaborative environment to transform theoretical concepts into real-world applications.

As part of our elite R&D team, you'll work on next-generation quantum systems, contribute to patentable innovations, and publish groundbreaking research in top-tier journals. We offer comprehensive benefits including stock options, flexible work arrangements, and continuous learning opportunities to keep you at the cutting edge.

Responsibilities

  • Design and implement novel quantum algorithms for optimization and simulation problems
  • Develop error correction protocols for scalable quantum systems
  • Collaborate with hardware engineers to prototype quantum processors
  • Lead research initiatives in quantum machine learning applications
  • Publish peer-reviewed papers and present findings at international conferences
  • Mentor junior researchers and drive cross-functional innovation

Qualifications

  • PhD in Physics, Computer Science, or related field with quantum focus
  • 3+ years of hands-on quantum computing research experience
  • Expertise in quantum programming languages (Qiskit, Cirq, or Q#)
  • Strong background in linear algebra and quantum mechanics
  • Proven track record of published quantum research
  • Experience with superconducting or ion-trap quantum systems
  • Ability to translate theoretical concepts into practical implementations

Required Skills

Quantum Computing Qiskit Cirq Q# Linear Algebra Quantum Mechanics Superconducting Systems Error Correction Machine Learning

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