Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution. We're pioneering the fusion of quantum computing and artificial intelligence to solve humanity's most complex challenges. As a Quantum AI Research Scientist, you'll architect next-gen algorithms that push the boundaries of computational possibility. Our state-of-the-art facility in San Francisco offers unparalleled resources and a collaborative environment where your innovations will shape tomorrow's digital landscape.
We seek visionary researchers who thrive at the intersection of quantum mechanics and machine learning. You'll collaborate with Nobel laureates and industry disruptors to develop quantum neural networks, optimize quantum-resistant cryptography, and pioneer real-time quantum simulation platforms. This isn't just a job—it's your chance to define the future of computation.
Responsibilities
- Design and implement quantum machine learning algorithms for breakthrough computational applications
- Develop hybrid quantum-classical systems to accelerate AI training by 1000x
- Lead research on quantum error correction for fault-tolerant AI operations
- Create novel quantum neural network architectures for pattern recognition and prediction
- Optimize quantum circuit synthesis for AI workloads on superconducting qubits
- Publish peer-reviewed research in top-tier quantum computing conferences
- Mentor junior researchers in quantum AI methodologies and best practices
Qualifications
- PhD in Quantum Computing, Physics, or Computer Science with 5+ years research experience
- Published work in Nature/Science or equivalent quantum computing journals
- Expertise in quantum algorithms (QAOA, VQE, Grover's) and quantum programming frameworks
- Proficiency in Python, C++, and quantum simulation tools (Qiskit, Cirq, PennyLane)
- Deep understanding of quantum machine learning and tensor network methods
- Experience with superconducting qubit systems or trapped-ion quantum processors
- Strong track record of translating theoretical research into practical implementations