Job Description
Join Nexus Quantum Labs at the forefront of technological revolution as we pioneer the next era of AI-driven quantum computing. This is your chance to architect solutions that will redefine industries by 2026. We're seeking a visionary Lead Quantum AI Engineer to bridge quantum mechanics and machine learning, transforming theoretical possibilities into tangible breakthroughs.
Our state-of-the-art lab in San Francisco offers an unparalleled environment where your expertise will directly impact the development of quantum neural networks, hybrid quantum-classical algorithms, and next-gen AI models. You'll collaborate with Nobel laureates and industry disruptors to solve humanity's most complex challenges.
This role includes competitive equity, unlimited learning stipends, and flexible work arrangements designed for peak innovation. If you're ready to shape the future of artificial intelligence, this is where you belong.
Responsibilities
- Design and implement quantum algorithms that accelerate machine learning workflows
- Lead development of hybrid quantum-classical AI systems for enterprise applications
- Architect scalable quantum neural networks for 2026-era computing environments
- Pioneer new error correction techniques for quantum machine learning models
- Mentor a team of quantum AI researchers and engineers
- Collaborate with hardware teams to optimize quantum-AI system integration
- Publish breakthrough research in top-tier AI and quantum computing journals
Qualifications
- PhD in Quantum Computing, Machine Learning, or related field with 5+ years industry experience
- Expertise in quantum programming languages (Q#, Qiskit, Cirq) and frameworks
- Proven track record of deploying production-level quantum AI solutions
- Deep understanding of quantum machine learning algorithms and theory
- Strong background in Python, TensorFlow/PyTorch, and high-performance computing
- Experience leading cross-functional technical teams in agile environments
- Published research in quantum computing or AI at NeurIPS/ICML/APS journals