Job Description
Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking a visionary Quantum Computing Research Lead to architect next-generation systems that will redefine computational boundaries. In this pivotal role, you'll lead a multidisciplinary team of physicists, engineers, and AI specialists to develop scalable quantum algorithms and error-correction frameworks. Our Austin-based innovation hub offers state-of-the-art facilities and unparalleled collaboration opportunities to transform theoretical breakthroughs into real-world applications.
This position requires deep expertise in quantum mechanics and a passion for solving humanity's most complex challenges. You'll work closely with industry partners to implement quantum solutions in cryptography, materials science, and climate modeling. We offer competitive compensation, comprehensive benefits, and a culture that celebrates intellectual curiosity and bold innovation.
Responsibilities
- Lead quantum algorithm development for commercial applications in cryptography and optimization
- Design and implement quantum error-correction protocols for fault-tolerant systems
- Direct cross-functional research teams in quantum hardware-software integration
- Collaborate with industry partners to deploy quantum solutions in high-impact domains
- Publish research in leading scientific journals and present at international conferences
- Secure external funding through government grants and private partnerships
- Develop quantum computing educational programs for academic and industry stakeholders
Qualifications
- PhD in Physics, Computer Science, or related field with 5+ years quantum research experience
- Proven expertise in quantum programming languages (Qiskit, Cirq, or Q#)
- Deep understanding of quantum error correction and fault-tolerant architectures
- Published research in quantum computing or quantum information theory
- Experience leading multidisciplinary technical teams in R&D environments
- Familiarity with quantum hardware platforms (superconducting, trapped ion, photonic)
- Strong background in machine learning and classical-quantum hybrid systems