Job Description
Join Nexus Quantum Dynamics at the forefront of technological evolution as we pioneer quantum computing solutions for the 2026 horizon. We're seeking a visionary Quantum Computing Architect to design and implement next-gen quantum systems that will redefine computational boundaries. In this pivotal role, you'll collaborate with Nobel laureates and industry disruptors to transform theoretical quantum algorithms into scalable, commercial-grade infrastructure. Our state-of-the-art lab in San Francisco offers unparalleled resources for pushing quantum capabilities beyond current limitations.
This position offers equity participation in our pre-IPO quantum breakthroughs and includes comprehensive benefits covering quantum-specific health protocols and relocation assistance for top global talent. You'll have direct influence on projects with NASA, DARPA, and Fortune 50 partners deploying quantum solutions by 2026.
Responsibilities
- Design fault-tolerant quantum computing architectures using topological qubits and photonic interconnects
- Develop quantum error correction protocols for 1000+ qubit systems
- Create hybrid quantum-classical software frameworks for enterprise integration
- Lead cross-functional teams of physicists, engineers, and AI specialists
- Architect quantum-resistant security protocols for 2026-era networks
- Optimize quantum algorithms for pharmaceutical and materials science applications
- Secure patents for novel quantum hardware innovations
Qualifications
- PhD in Quantum Physics, Computer Engineering, or equivalent experience
- 5+ years in quantum computing with demonstrated 50+ qubit system deployments
- Expertise in quantum error correction and topological quantum computing
- Proficiency in Qiskit, Cirq, or equivalent quantum programming frameworks
- Published research in Nature/Science on quantum coherence or entanglement
- Certification in quantum cryptography from recognized institutions
- Experience with cryogenic quantum processor design and control systems
- Strong background in machine learning for quantum state tomography