Job Description
Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer the next generation of quantum computing systems. We're seeking a visionary 2026 Quantum Computing Research Lead to architect breakthrough solutions that will redefine computational paradigms. This role offers unparalleled opportunity to shape the future of AI, cryptography, and materials science while working with cutting-edge hardware and world-class research teams.
Our Austin headquarters features state-of-the-art quantum annealing facilities and collaborative innovation labs. You'll lead a cross-functional team of physicists, engineers, and data scientists to develop scalable quantum architectures that solve previously impossible problems. This position includes competitive equity, comprehensive benefits, and dedicated R&D funding for experimental initiatives.
Responsibilities
- Design and implement quantum algorithms for complex optimization problems in finance and logistics
- Lead research into fault-tolerant quantum computing architectures for 2026 deployment
- Collaborate with hardware teams to develop error-correction protocols for 1000+ qubit systems
- Publish high-impact research in peer-reviewed journals and industry conferences
- Secure government and corporate partnerships for quantum computing applications
- Mentor junior researchers and establish best practices for quantum software development
- Develop roadmap for quantum integration with classical AI frameworks
Qualifications
- PhD in Quantum Computing, Theoretical Physics, or related field with 5+ years industry experience
- Proven expertise in quantum algorithm design (QAOA, VQE, Grover's variants)
- Published research in quantum error correction or topological qubit systems
- Proficiency in quantum programming frameworks (Qiskit, Cirq, Q#)
- Demonstrated success leading multidisciplinary technical teams
- Strong background in superconducting qubit or ion trap technologies
- Experience securing DoD or NSF research grants
- Expertise in quantum machine learning integration