Job Description
Join Nexus Future Labs at the forefront of 2026's technological revolution. We're seeking a visionary Quantum AI Research Scientist to pioneer breakthroughs at the intersection of quantum computing and artificial intelligence. Shape the future of computational intelligence in our state-of-the-art Austin facility, where you'll collaborate with Nobel laureates and industry disruptors. This role offers unparalleled opportunities to publish groundbreaking research, patent transformative technologies, and directly impact how humanity solves complex global challenges.
As part of our elite research division, you'll access quantum hardware platforms, petabyte-scale datasets, and a $50M annual R&D budget. We provide comprehensive benefits including equity grants, flexible remote work options, and continuous learning stipends. If you're ready to push beyond conventional boundaries and define what's possible in 2026, this is your calling.
Responsibilities
- Design and implement quantum machine learning algorithms for next-gen AI systems
- Lead cross-functional research teams in developing hybrid quantum-classical neural networks
- Author high-impact publications for Nature/Science journals and industry whitepapers
- Collaborate with hardware teams to optimize quantum algorithms for near-term devices
- Translate theoretical breakthroughs into patentable technologies with commercial applications
- Mentor PhD researchers and foster innovation in quantum AI methodologies
- Secure federal grants and industry partnerships to advance quantum AI research initiatives
Qualifications
- PhD in Quantum Computing, Theoretical Physics, or Machine Learning (or equivalent research experience)
- 3+ years of hands-on experience with quantum programming frameworks (Qiskit, Cirq, or Q#)
- Published research in quantum machine learning or quantum information theory
- Expertise in Python, TensorFlow/PyTorch, and high-performance computing environments
- Deep understanding of quantum algorithms (Shor's, Grover's, VQE) and error mitigation techniques
- Proven ability to lead complex research projects with measurable outcomes
- Strong background in linear algebra, probability theory, and computational complexity