Job Description
Join Nexus Labs at the forefront of technological evolution as we pioneer the convergence of quantum computing and artificial intelligence. We're seeking a visionary Quantum AI Research Scientist to architect the next generation of computational paradigms that will redefine industries by 2026.
In this cutting-edge role, you'll collaborate with Nobel laureates and Turing Award winners to develop hybrid quantum-neural systems that solve previously impossible problems. Our state-of-the-art facility in San Francisco houses the world's most advanced quantum processors, and we offer unparalleled resources for groundbreaking research.
What we offer: Equity packages worth up to $500,000, flexible hybrid work arrangements, unlimited research budget, and access to our exclusive Quantum Innovation Lab. This isn't just a job—it's your chance to shape humanity's technological future.
Responsibilities
- Design and implement novel quantum machine learning algorithms for optimization and pattern recognition
- Develop hybrid quantum-classical neural networks achieving quantum advantage
- Lead cross-functional teams in prototyping quantum AI applications for cryptography and materials science
- Publish breakthrough research in Nature/Science journals and present at IEEE Quantum Week
- Architect fault-tolerant quantum computing systems operating at room temperature
- Mentor PhD candidates and secure $10M+ in government/industry research grants
Qualifications
- PhD in Quantum Computing, Physics, or Computer Science with 5+ years research experience
- Published work in top-tier quantum computing or AI conferences (e.g., QIP, NeurIPS)
- Expertise in quantum circuit design, error correction, and quantum algorithm development
- Proficiency in quantum programming frameworks (Qiskit, Cirq) and AI frameworks (PyTorch)
- Deep understanding of quantum supremacy benchmarks and quantum advantage metrics
- Track record of translating theoretical concepts into working quantum prototypes
- Strong background in linear algebra, probability, and computational complexity theory