Job Description
Join Nexus Future Labs at the forefront of 2026's technological revolution. We're pioneering quantum-AI integration for next-generation computational breakthroughs. As a Quantum AI Research Scientist, you'll design novel algorithms that bridge quantum mechanics and artificial intelligence, solving previously unsolvable problems in cryptography, materials science, and autonomous systems. Our state-of-the-art Austin campus offers unparalleled resources for experimental quantum computing and AI model training.
We're seeking visionary researchers who thrive at the intersection of physics, computer science, and machine learning. This role offers competitive equity packages, flexible hybrid work arrangements, and access to our exclusive 'Future Thinkers' innovation incubator. Collaborate with Nobel laureates and Turing Award winners in an environment where theoretical breakthroughs become commercial reality.
Responsibilities
- Design and implement quantum-AI hybrid algorithms for computational optimization
- Lead experimental validation of quantum neural networks on fault-tolerant quantum processors
- Develop novel error-correction techniques for quantum machine learning models
- Collaborate with hardware teams to co-design quantum-AI accelerator architectures
- Publish breakthrough research in top-tier journals and conferences (Nature, NeurIPS, etc.)
- Mentor junior researchers in quantum computing principles and AI methodologies
- Secure federal and private research funding for quantum-AI initiatives
Qualifications
- PhD in Quantum Computing, Theoretical Computer Science, or Physics (or equivalent experience)
- 3+ years of hands-on quantum algorithm development using Qiskit/Cirq
- Expertise in deep learning frameworks (PyTorch/TensorFlow) with quantum extensions
- Published research in quantum machine learning or quantum complexity theory
- Proficiency in high-performance computing environments and parallel programming
- Experience with quantum hardware interfaces (IBM Quantum, Rigetti, etc.)
- Strong background in linear algebra, probability theory, and information theory