Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution. We're seeking visionary Quantum AI Research Scientists to pioneer breakthroughs at the intersection of quantum computing and artificial intelligence. Shape the future of computational science by developing hybrid quantum-classical algorithms, optimizing quantum machine learning models, and solving previously unsolvable problems in cryptography, materials science, and complex systems. Collaborate with Nobel laureates and industry disruptors in our state-of-the-art San Francisco research facility. This role offers unparalleled opportunities to publish groundbreaking research, lead cross-disciplinary projects, and influence the next wave of technological evolution.
Responsibilities
- Design and implement novel quantum algorithms for AI optimization and pattern recognition
- Develop hybrid quantum-classical frameworks for enhanced machine learning performance
- Lead research initiatives in quantum error correction and fault-tolerant computing
- Collaborate with hardware teams to translate theoretical models into practical quantum applications
- Publish high-impact research in top-tier journals and present at global conferences
- Mentor junior researchers and drive innovation in quantum AI methodologies
- Secure external funding through NSF, DARPA, and industry partnerships
Qualifications
- PhD in Quantum Physics, Computer Science, or Computational Mathematics (or equivalent experience)
- Proven expertise in quantum programming languages (Q#, Qiskit, Cirq) and quantum circuit design
- Strong background in machine learning frameworks (TensorFlow, PyTorch) and classical algorithm optimization
- Publication record in quantum computing or AI research (Nature, Science, or IEEE journals)
- Experience with quantum hardware platforms (IBM Quantum, Rigetti, IonQ) and cloud quantum services
- Deep understanding of quantum information theory and complexity analysis
- Exceptional problem-solving skills and ability to work in high-impact, multidisciplinary teams