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Quantum Computing Research Scientist

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
1 Juli 2026
Deadline
1 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 a visionary Research Scientist to develop scalable quantum algorithms and hardware interfaces that will redefine computational capabilities. Our state-of-the-art facility in San Francisco offers unparalleled resources for quantum experimentation, with competitive benefits including equity, flexible work arrangements, and access to industry-leading mentors. Help us solve humanity's most complex challenges while advancing the quantum frontier.

Responsibilities

  • Design and implement quantum algorithms for optimization and machine learning applications
  • Collaborate with hardware teams to develop error correction protocols for fault-tolerant systems
  • Lead experimental validation of quantum circuits on next-generation processors
  • Publish breakthrough research in peer-reviewed journals and industry conferences
  • Mentor junior researchers and cross-functional teams on quantum principles
  • Secure federal and private funding for advanced quantum research initiatives

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field (MS with exceptional experience considered)
  • 3+ years of hands-on experience with quantum programming frameworks (Qiskit, Cirq, or Q#)
  • Published research in quantum information science or top-tier computational journals
  • Expertise in quantum error correction and fault-tolerant architectures
  • Strong background in linear algebra, probability theory, and computational complexity
  • Proficiency with high-performance computing environments and parallelization techniques
  • US citizenship or permanent residency required for government project access

Required Skills

Quantum Computing Qiskit Cirq Error Correction Quantum Algorithms Machine Learning High-Performance Computing Research

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