Job Description
Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer next-generation AI systems powered by quantum computing. We're seeking a visionary Quantum AI Systems Architect to design and implement revolutionary solutions that will redefine industries by 2026. In this role, you'll collaborate with Nobel laureates and Silicon Valley innovators to bridge quantum mechanics with artificial intelligence, creating scalable frameworks for autonomous systems, predictive analytics, and computational breakthroughs.
Our cutting-edge lab features state-of-the-art quantum processors and AI development environments. You'll lead projects with global impact while enjoying competitive compensation, flexible work arrangements, and continuous learning opportunities. This is your chance to shape the technological landscape of tomorrow.
Responsibilities
- Design and implement hybrid quantum-classical AI architectures for enterprise applications
- Develop error-corrected quantum neural networks achieving fault-tolerant computation
- Lead R&D initiatives in quantum machine learning algorithms and optimization techniques
- Collaborate with hardware teams to co-design quantum processors optimized for AI workloads
- Translate complex quantum computing concepts into scalable engineering solutions
- Drive innovation in quantum-safe cryptography for AI systems
- Mentor cross-functional teams in quantum AI integration methodologies
Qualifications
- PhD in Quantum Computing, Computer Science, or Physics with 5+ years industry experience
- Expertise in quantum algorithms (Shor's, Grover's, VQE) and quantum error correction
- Proficiency in quantum programming languages (Q#, Qiskit, Cirq) and AI frameworks
- Published research in quantum machine learning or quantum information theory
- Experience with large-scale distributed computing systems and cloud platforms (AWS/Azure)
- Demonstrated ability to lead technical teams and deliver complex projects
- Strong background in linear algebra, probability theory, and computational complexity