Job Description
Join Nexus Labs at the frontier of technological evolution as we pioneer solutions for 2026 and beyond. We're seeking a visionary Quantum AI Architect to design next-generation systems that bridge quantum computing and artificial intelligence. This role offers unparalleled opportunities to shape the future of computational intelligence while working with Nobel Prize-winning researchers in our state-of-the-art facility.
You'll lead cross-disciplinary teams to develop fault-tolerant quantum neural networks, optimize AI algorithms for quantum advantage, and create scalable frameworks for real-world applications in cryptography, materials science, and climate modeling. Our culture combines academic rigor with entrepreneurial energy, offering competitive equity packages and flexible work arrangements.
Responsibilities
- Design and implement quantum machine learning architectures for 2026-era applications
- Lead development of hybrid quantum-classical AI systems with 99.99% reliability
- Collaborate with physicists to develop quantum error correction protocols for neural networks
- Create blueprints for AI-powered quantum optimization tools solving complex global challenges
- Mentor researchers in quantum algorithm development and quantum-safe AI frameworks
- Present breakthrough findings at premier conferences including IEEE Quantum Week and NeurIPS
- Drive innovation in quantum-inspired AI techniques for near-term quantum devices
Qualifications
- PhD in Quantum Computing, Machine Learning, or Computational Physics (or equivalent experience)
- 3+ years developing quantum algorithms or AI systems with quantum advantage
- Expertise in quantum programming languages (Q#, Cirq, or Qiskit) and frameworks
- Published research in top-tier journals (Nature, Science, or IEEE) on quantum/AI convergence
- Deep understanding of quantum error correction and fault-tolerant architectures
- Proven ability to lead technical teams in high-stakes R&D environments
- Strong background in cryptography, optimization theory, or complex systems modeling