Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution! We're seeking a visionary Quantum Computing Architect to design next-gen quantum systems that will redefine computing paradigms. This role offers unparalleled opportunities to shape the future of quantum algorithms, error correction, and scalable hardware integration. You'll collaborate with Nobel laureates and industry pioneers in our state-of-the-art Austin facility, pushing the boundaries of what's computationally possible.
Our team operates at the intersection of physics, computer science, and materials engineering, creating solutions for cryptography, drug discovery, and climate modeling challenges. With competitive benefits, research grants, and equity opportunities, this is your chance to build quantum systems that will power humanity's next great leaps.
Responsibilities
- Design fault-tolerant quantum architectures for 2026-era computational challenges
- Develop hybrid quantum-classical integration frameworks for enterprise applications
- Lead quantum error correction protocol optimization for 1000+ qubit systems
- Collaborate with hardware teams to co-design quantum processors and control systems
- Create quantum software development kits (SDKs) for developer ecosystem expansion
- Establish security protocols for quantum-resistant cryptography implementations
- Mentor cross-functional teams in quantum algorithm development and optimization
Qualifications
- PhD in Quantum Computing, Physics, or Computer Science with 5+ years industry experience
- Expertise in quantum circuit design and topological quantum computing models
- Proven track record of publishing in Nature/Science journals or equivalent
- Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
- Experience with cryogenic systems and quantum hardware integration
- Deep understanding of quantum error correction codes (surface, LDPC, etc.)
- Demonstrated ability to lead technical projects with multi-disciplinary teams
- Strong background in machine learning for quantum system optimization