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Quantum Computing Research Scientist

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

Job Description

Join Nexus Labs at the forefront of computational revolution as we pioneer quantum solutions for 2026's most pressing challenges. We're seeking a visionary Quantum Computing Research Scientist to develop scalable quantum algorithms and accelerate practical applications in cryptography, materials science, and AI optimization. Our state-of-the-art facility in San Francisco offers unparalleled resources and collaborative freedom to transform theoretical breakthroughs into real-world impact.

This role provides equity grants, comprehensive benefits, and dedicated R&D budget for experimental projects. You'll collaborate with Nobel laureates and industry pioneers while shaping the quantum landscape for the next decade.

Responsibilities

  • Design and implement novel quantum algorithms for complex optimization problems
  • Lead cross-functional teams to prototype quantum solutions for enterprise clients
  • Publish research in peer-reviewed journals and present at international conferences
  • Develop error-correction techniques to enhance quantum system stability
  • Collaborate with hardware teams to optimize quantum circuit performance
  • Mentor junior researchers and drive innovation in quantum machine learning

Qualifications

  • PhD in Quantum Computing, Physics, Computer Science, or related field
  • 3+ years of hands-on quantum algorithm development experience
  • Proficiency with quantum programming frameworks (Qiskit, Cirq, or Q#)
  • Published research in quantum information science or high-impact journals
  • Deep understanding of quantum error correction and fault tolerance
  • Experience with quantum hardware platforms (IBM Quantum, Rigetti, or IonQ)
  • Strong analytical skills with ability to translate complex mathematical concepts

Required Skills

Quantum Computing Algorithm Development Quantum Programming Research Cryptography Machine Learning Error Correction

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