Job Description
Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer the next generation of AI-quantum integration. We're seeking a visionary Quantum AI Research Scientist to develop breakthrough algorithms that will redefine computing paradigms by 2026. In this role, you'll collaborate with Nobel laureates and industry disruptors to solve humanity's most complex challenges through quantum neural networks and hybrid quantum-classical systems.
Our state-of-the-art facility in San Francisco offers unparalleled resources including 128-qubit quantum processors and exascale computing clusters. We provide competitive equity packages, unlimited research budgets, and flexible work arrangements designed to maximize innovation.
Responsibilities
- Design and implement novel quantum machine learning algorithms for real-world optimization problems
- Lead cross-functional teams in developing hybrid quantum-classical frameworks for 2026-era applications
- Publish groundbreaking research in top-tier journals and present at premier international conferences
- Collaborate with hardware engineers to co-design quantum processors optimized for AI workloads
- Mentor junior researchers and establish best practices for quantum AI development protocols
- Secure patents and intellectual property for proprietary quantum AI methodologies
- Interface with government agencies and industry partners to shape quantum AI standards
Qualifications
- PhD in Quantum Computing, Machine Learning, or Physics with 5+ years of research experience
- Expertise in quantum algorithms (QAOA, VQE, quantum neural networks) and error correction
- Proficiency in quantum programming languages (Qiskit, Cirq, Q#) and classical ML frameworks
- Published record in quantum computing or AI journals with 15+ citations
- Experience with quantum hardware (IBM Q, Rigetti, D-Wave) and cloud quantum platforms
- Demonstrated ability to translate theoretical concepts into scalable implementations
- Strong background in linear algebra, probability theory, and computational complexity