Job Description
Join the frontier of technological evolution at Nexus Quantum Labs, where we're pioneering the next wave of human-computer interaction. As a Quantum AI Research Scientist, you'll architect the algorithms that will power 2026's most disruptive applications—from neural-quantum hybrids to predictive AI systems operating at the edge of computational possibility.
We're seeking visionary minds to dismantle conventional computing paradigms. In this Austin-based role, you'll collaborate with Nobel-caliber physicists and deep learning specialists to develop proprietary quantum neural networks that process information at near-instantaneous speeds. Your work will directly influence how industries solve previously unsolvable challenges in materials science, climate modeling, and personalized medicine.
Nexus Quantum Labs offers unparalleled resources: access to 256-qubit quantum processors, a $50M R&D budget, and partnerships with NASA and MIT. We provide relocation stipends, equity packages, and flexible hybrid work arrangements to attract the world's brightest innovators.
Responsibilities
- Design and implement quantum-enhanced machine learning frameworks for next-gen predictive analytics
- Develop proprietary quantum algorithms to optimize neural network training processes
- Lead cross-disciplinary research teams combining quantum physics, computational neuroscience, and AI
- Create patent-pending methodologies for quantum-resistant cryptographic protocols
- Author breakthrough research for Nature Quantum and IEEE journals
- Architect hybrid quantum-classical computing architectures for enterprise applications
- Collaborate with hardware teams to co-design quantum processors optimized for AI workloads
Qualifications
- PhD in Quantum Computing, Theoretical Physics, or Computational Mathematics (postdoc preferred)
- 3+ years of research experience with quantum machine learning algorithms
- Proficiency in Qiskit, Cirq, or quantum programming frameworks
- Published record in top-tier quantum/AI journals (minimum 5 papers)
- Expertise in tensor networks and quantum error correction techniques
- Demonstrated experience with high-performance computing (HPC) clusters
- Strong background in Bayesian inference and stochastic optimization
- Portfolio of open-source quantum AI contributions