Job Description
Join Nexus Labs at the forefront of quantum computing innovation as we pioneer breakthrough applications for 2026 and beyond. We're seeking a visionary Quantum Computing Research Scientist to develop next-generation algorithms and solve complex computational challenges that will redefine industries. Our state-of-the-art facility in San Francisco offers unparalleled resources for quantum experimentation and collaborative research. This role provides the unique opportunity to shape the future of technology while working alongside Nobel laureates and industry pioneers.
We offer competitive compensation, comprehensive benefits, and flexible work arrangements to support our team's success. If you're passionate about pushing the boundaries of quantum mechanics and practical implementation, we encourage you to apply.
Responsibilities
- Design and implement novel quantum algorithms for optimization, cryptography, and machine learning applications
- Lead experimental quantum computing projects using superconducting qubit systems
- Collaborate with cross-functional teams to develop quantum-classical hybrid computing frameworks
- Publish research findings in top-tier journals and present at international conferences
- Drive the development of quantum error correction techniques for practical deployment
- Secure external research funding through NSF and DoD grants
- Mentor junior researchers and contribute to patent portfolios
Qualifications
- PhD in Quantum Computing, Physics, Computer Science, or related field
- 3+ years of hands-on experience with quantum programming languages (Qiskit, Cirq, or Q#)
- Published research in quantum information science or quantum algorithms
- Deep understanding of quantum error correction and fault-tolerant computing
- Expertise in superconducting qubit systems or ion trap technologies
- Strong background in linear algebra, probability theory, and complexity theory
- Experience with high-performance computing environments and parallel processing
- Ability to translate theoretical concepts into practical experimental implementations