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Quantum Computing Research Scientist 2026

Nexus Labs
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Join Nexus Labs at the forefront of technological evolution as we pioneer quantum computing solutions that will redefine 2026's digital landscape. We seek a visionary Research Scientist to lead breakthrough initiatives in quantum algorithms, cryptography, and computational theory. Collaborate with Nobel laureates and industry pioneers in our state-of-the-art facility overlooking the Bay, where innovation meets impact.

As part of our elite Future Technologies Division, you'll develop scalable quantum systems that solve previously impossible problems across finance, medicine, and AI. Our commitment to ethical innovation ensures your work shapes a more efficient, secure, and equitable future.

Responsibilities

  • Design and implement novel quantum algorithms for optimization and machine learning applications
  • Lead cross-functional teams to prototype quantum-resistant cryptographic systems
  • Author peer-reviewed publications and patent quantum computing methodologies
  • Develop error-correction techniques for fault-tolerant quantum processors
  • Collaborate with hardware teams to optimize quantum circuit performance
  • Present breakthrough findings at premier quantum computing conferences
  • Guide junior researchers through mentorship programs

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science
  • 3+ years of hands-on quantum algorithm development experience
  • Proficiency in quantum programming languages (Qiskit, Cirq)
  • Published research in top-tier journals (Nature, Science, etc.)
  • Expertise in quantum error correction and topological quantum computing
  • Strong background in linear algebra and quantum information theory
  • Proven ability to translate complex quantum concepts into practical applications

Required Skills

Quantum Computing Quantum Algorithms Cryptography Qiskit Cirq Quantum Error Correction Machine Learning Theoretical Physics

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