Job Description
Join Nexus Labs at the forefront of computational revolution as we pioneer the next generation of quantum systems. We're seeking a visionary Quantum Computing Architect to design scalable quantum algorithms and hybrid classical-quantum solutions that will redefine industries by 2026. This role offers unparalleled opportunity to shape the future of technology in our state-of-the-art Austin research facility.
Our team operates at the intersection of theoretical physics, advanced mathematics, and practical engineering. You'll collaborate with Nobel laureates, industry disruptors, and government partners to develop quantum-resistant encryption, quantum machine learning frameworks, and fault-tolerant quantum processors. We provide competitive equity packages, unlimited R&D budget, and access to the world's most powerful quantum simulators.
Responsibilities
- Architect scalable quantum algorithms for optimization, simulation, and machine learning applications
- Design hybrid classical-quantum computing frameworks targeting 2026 deployment milestones
- Lead development of quantum error correction protocols achieving >99.9% fidelity
- Collaborate with hardware teams to co-design quantum processors with 1000+ qubit capabilities
- Translate complex quantum concepts into implementable solutions for Fortune 500 partners
- Drive innovation in quantum cryptography and post-quantum security standards
- Present breakthrough research at international quantum computing conferences
Qualifications
- PhD in Quantum Physics, Computer Science, or related field (or equivalent experience)
- 5+ years developing quantum algorithms or quantum-classical hybrid systems
- Expertise in quantum programming languages (Qiskit, Cirq, Q#) and circuit optimization
- Proven track record publishing in peer-reviewed quantum computing journals
- Deep understanding of quantum error correction and fault-tolerant architectures
- Experience with quantum hardware platforms (IBM Quantum, Rigetti, IonQ)
- Strong background in linear algebra, group theory, and computational complexity