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Information Technology 🏢 Full Time ⭐️ Verified

Quantum Computing Researcher

Nexus Labs
San Francisco
Estimated Salary
USD 150.000 – USD 220.000
Live Update
23 Mei 2026
Deadline
23 Mei 2027

Job Description

Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. Our interdisciplinary team of visionaries is developing next-gen algorithms to solve humanity's most complex challenges. This role offers unparalleled opportunity to shape the future of computational science while working with cutting-edge hardware and collaborating with Nobel laureates. We provide competitive equity packages, flexible work arrangements, and dedicated R&D resources for breakthrough innovations.

Responsibilities

  • Design and implement quantum algorithms for practical applications in cryptography and optimization
  • Collaborate with hardware engineers to optimize quantum circuit performance on prototype systems
  • Lead research on quantum error correction techniques for fault-tolerant computing
  • Publish peer-reviewed papers and present findings at international conferences
  • Develop roadmaps for quantum advantage implementation in enterprise environments
  • Mentor junior researchers and foster cross-functional innovation

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (MS with exceptional experience considered)
  • 3+ years of hands-on experience with quantum programming frameworks (Qiskit, Cirq, or Q#)
  • Publication record in top-tier quantum computing journals
  • Expertise in quantum information theory and many-body physics
  • Proficiency in high-performance computing environments and parallel programming
  • Demonstrated ability to translate theoretical concepts into practical implementations
  • Strong communication skills for technical and non-technical stakeholders

Required Skills

quantum computing quantum algorithms quantum error correction cryptography optimization algorithms Qiskit Cirq high-performance computing machine learning

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