Job Description
Join Nexus Future Labs at the forefront of 2026's technological revolution. We're pioneering quantum-AI integration solutions that will redefine industries worldwide. As a Quantum AI Research Scientist, you'll architect next-gen algorithms that leverage quantum supremacy to solve previously impossible computational challenges. Our state-of-the-art facility in San Francisco offers unparalleled resources to transform theoretical breakthroughs into market-ready innovations.
We're seeking visionaries who thrive at the intersection of quantum mechanics and machine learning. You'll collaborate with Nobel laureates and industry disruptors to develop proprietary quantum neural networks, optimize quantum-resistant cryptography, and pioneer autonomous quantum systems. This role offers patent opportunities, conference leadership, and direct impact on shaping our quantum future.
Responsibilities
- Design and implement quantum machine learning algorithms for 2026-era computational challenges
- Lead research initiatives in quantum neural network optimization and quantum-resistant cryptography
- Develop proprietary quantum simulation frameworks using Qiskit and Cirq
- Collaborate with hardware teams to co-design quantum-AI hybrid systems
- Publish breakthrough research in Nature/Science journals and lead industry conferences
- Mentor junior researchers in quantum computing best practices
- Translate theoretical models into scalable production-ready quantum applications
Qualifications
- PhD in Quantum Computing, Theoretical Physics, or Machine Learning with 5+ years research experience
- Expertise in quantum algorithms (Shor's, Grover's, VQE) and quantum error correction
- Proficiency with quantum programming frameworks (Qiskit, Cirq, Q#) and Python/C++
- Published work in quantum machine learning or quantum AI integration
- Deep understanding of NISQ-era limitations and fault-tolerant quantum computing
- Experience with quantum hardware platforms (IBM Quantum, Rigetti, IonQ)
- Strong background in linear algebra, probability theory, and computational complexity