Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution! We're seeking a visionary Quantum AI Integration Specialist to architect the next generation of hybrid quantum-classical systems. This role bridges bleeding-edge quantum computing with transformative artificial intelligence, solving challenges once deemed impossible. You'll collaborate with Nobel laureates and industry pioneers in our Austin R&D hub, where your work will directly shape the future of computational science.
Our ideal candidate thrives at the intersection of theoretical physics and practical AI implementation. You'll develop quantum-enhanced machine learning algorithms, optimize quantum neural networks, and pioneer novel hybrid architectures. This is more than a job—it's your chance to redefine technological boundaries in an environment that celebrates audacious innovation and intellectual curiosity.
Responsibilities
- Design and implement quantum-classical hybrid algorithms for AI acceleration
- Develop error-corrected quantum neural networks for pattern recognition
- Optimize quantum machine learning frameworks for real-world deployment
- Collaborate on quantum-resistant cryptography systems for 2026-era security
- Lead research on quantum-enhanced natural language processing models
- Translate theoretical quantum computing concepts into production-ready AI solutions
- Contribute to open-source quantum AI frameworks and publish breakthrough research
Qualifications
- PhD in Quantum Computing, Physics, or Computer Science (or equivalent experience)
- Expertise in quantum circuit design and error correction methodologies
- Proficiency with quantum programming frameworks (Qiskit, Cirq, PennyLane)
- Strong background in machine learning frameworks (TensorFlow, PyTorch)
- Experience with quantum hardware integration (IBM Quantum, Rigetti, IonQ)
- Published research in quantum machine learning or quantum algorithms
- Demonstrated ability to lead cross-disciplinary technical projects
- Deep understanding of quantum information theory principles