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Quantum Computing Research Lead

Nexus Quantum Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
Live Update
15 Mei 2026
Deadline
15 Mei 2027

Job Description

Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer the next generation of quantum computing systems. As we approach 2026, our multidisciplinary team is developing breakthrough applications in cryptography, AI optimization, and materials science. This role offers the unique opportunity to shape the quantum landscape while collaborating with Nobel laureates and industry pioneers.

We provide competitive equity packages, flexible remote work options, and access to cutting-edge quantum hardware. Our state-of-the-art facility in San Francisco's innovation district fosters an environment where groundbreaking ideas become reality.

Responsibilities

  • Lead quantum algorithm development for 2026-era computational challenges
  • Design and optimize quantum circuits for error-corrected systems
  • Collaborate with AI teams to hybridize quantum-classical workflows
  • Publish peer-reviewed research in top-tier quantum computing journals
  • Mentor junior researchers and drive cross-functional innovation
  • Secure patents for novel quantum methodologies
  • Develop roadmaps for quantum advantage in real-world applications

Qualifications

  • PhD in Quantum Physics, Computer Science, or related field
  • 5+ years of hands-on quantum algorithm development experience
  • Expertise in quantum programming languages (Qiskit, Cirq, Q#)
  • Published research in quantum error correction or fault tolerance
  • Proficiency in Python/C++ with high-performance computing experience
  • Strong background in linear algebra and quantum information theory
  • Experience with quantum hardware platforms (IBM, Rigetti, IonQ)
  • Track record of translating theoretical concepts to practical implementations

Required Skills

Quantum Computing Quantum Algorithms Qiskit Cirq Q# Python C++ Quantum Error Correction Fault Tolerance Quantum Machine Learning

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