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Quantum Computing Research Scientist

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 280.000
New
Live Update
21 Mei 2026
Deadline
21 Mei 2027

Job Description

Join Nexus Quantum Labs at the forefront of computational revolution. We're pioneering quantum algorithms that will redefine 2026's technological landscape. As a Research Scientist, you'll architect breakthrough solutions in cryptography, optimization, and machine learning. Collaborate with Nobel laureates in our state-of-the-art facility in San Francisco's Innovation District. Your work will directly impact industries from healthcare to aerospace, solving previously unsolvable problems. We offer unparalleled resources, flexible research parameters, and competitive equity packages. This isn't just a jobβ€”it's your chance to shape humanity's computational future.

Responsibilities

  • Design and implement novel quantum algorithms for real-world applications
  • Lead experimental validation of quantum circuits using superconducting platforms
  • Develop error-correction protocols for fault-tolerant quantum systems
  • Collaborate with industry partners to prototype quantum solutions
  • Publish breakthrough research in top-tier journals and conferences
  • Mentor junior researchers in quantum information theory
  • Secure patents for proprietary quantum methodologies

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 3+ years of hands-on quantum computing research experience
  • Proficiency with Qiskit, Cirq, or similar quantum frameworks
  • Published work in Nature/Science or equivalent tier journals
  • Deep understanding of quantum error correction and fault tolerance
  • Experience with cryogenic quantum hardware systems
  • Strong background in linear algebra and quantum information theory
  • Track record of translating theoretical models to practical implementations

Required Skills

Quantum Computing Quantum Algorithms Cryogenic Systems Error Correction Qiskit Cirq Quantum Cryptography Machine Learning Fault-Tolerant Design

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