Job Description
Join Nexus Quantum Labs at the forefront of technological revolution. We're seeking a visionary 2026 Quantum AI Architect to pioneer the convergence of quantum computing and artificial intelligence. Shape tomorrow's computational landscape by designing next-generation quantum neural networks and hybrid AI systems. This role offers unparalleled opportunities to work with Nobel Prize-winning researchers and deploy solutions that will redefine industries.
Why Nexus Quantum Labs? We're the incubator for humanity's quantum leap, backed by $2B in venture capital and partnerships with NASA, IBM Quantum, and MIT. Our San Francisco hub features a 50-qubit quantum processor lab and the world's first AI-optimized cryogenic computing facility.
Responsibilities
- Architect quantum AI frameworks for 2026-era applications including molecular simulation and climate modeling
- Develop hybrid quantum-classical machine learning pipelines achieving 1000x speedup over classical systems
- Lead cross-functional teams of quantum physicists and ML engineers to prototype breakthrough algorithms
- Secure patents for novel quantum neural network topologies and error-correction methodologies
- Collaborate with NASA on quantum deep-space communication protocols
- Publish peer-reviewed research in Nature Quantum and IEEE Quantum journals
- Present findings at global quantum summits including Q2B and Quantum Future Conference
Qualifications
- PhD in Quantum Computing, AI, or Computational Physics with 5+ years industry experience
- Published research in quantum machine learning or quantum neural networks
- Proficiency in quantum programming languages (Qiskit, Cirq, Q#) and AI frameworks (PyTorch, TensorFlow)
- Expertise in quantum error correction codes and fault-tolerant architectures
- Track record of deploying quantum algorithms on real quantum processors
- Strong background in complex systems theory and emergent behavior modeling
- Experience securing SBIR grants or DARPA quantum research funding
- Fluency in quantum hardware optimization for superconducting and photonic systems