Job Description
Join Nexus Future Labs at the forefront of 2026's quantum revolution. We're pioneering the next generation of computational infrastructure that will redefine cryptography, AI, and scientific discovery. As our Quantum Computing Architect, you'll design and implement scalable quantum systems that solve previously impossible challenges.
This role demands a blend of visionary thinking and hands-on expertise. You'll lead cross-disciplinary teams in developing error-corrected qubits, quantum algorithms, and hybrid quantum-classical interfaces. Your work will directly impact breakthroughs in materials science, financial modeling, and climate prediction.
We offer competitive equity packages, flexible remote work options, and access to our state-of-the-art quantum research facilities. If you're ready to shape the computational paradigm of tomorrow, we want to hear from you.
Responsibilities
- Design and implement fault-tolerant quantum computing architectures for 2026-era applications
- Develop quantum algorithms optimized for real-world business and scientific challenges
- Lead integration of quantum processors with classical high-performance computing systems
- Establish quantum security protocols for next-gen data encryption and blockchain networks
- Collaborate with AI research teams to develop quantum-enhanced machine learning models
- Document quantum system specifications and create implementation roadmaps
- Mentor junior quantum engineers on cutting-edge quantum error correction techniques
Qualifications
- PhD in Quantum Physics, Computer Science, or related field (or equivalent experience)
- 5+ years in quantum computing architecture or quantum algorithm development
- Proficiency with quantum programming languages (Q#, Qiskit, Cirq)
- Experience with superconducting qubits, ion traps, or photonic quantum systems
- Strong background in error correction codes and fault-tolerant quantum computing
- Demonstrated success in publishing quantum computing research in peer-reviewed journals
- Expertise in optimizing quantum algorithms for near-term hardware constraints
- Knowledge of quantum machine learning and hybrid quantum-classical workflows