Job Description
Join Nexus Dynamics at the forefront of technological evolution as we pioneer the next generation of quantum-powered AI systems. We're seeking visionary Quantum AI Research Scientists to develop groundbreaking algorithms that will redefine computational boundaries in 2026 and beyond. Our Austin-based innovation lab offers unparalleled resources and a collaborative environment where your expertise in quantum mechanics and artificial intelligence will shape the future of technology.
As part of our elite research team, you'll work on cutting-edge quantum machine learning models, design fault-tolerant quantum circuits, and contribute to projects that solve previously unsolvable computational challenges. We offer competitive compensation, comprehensive benefits, and the opportunity to publish groundbreaking research in leading scientific journals.
Responsibilities
- Design and implement quantum algorithms for machine learning and AI optimization
- Develop hybrid quantum-classical computing architectures for enterprise applications
- Lead research initiatives in quantum neural networks and quantum-enhanced data processing
- Collaborate with cross-functional teams to integrate quantum solutions into real-world products
- Publish peer-reviewed research and present findings at international conferences
- Stay current with emerging quantum computing technologies and industry trends
- Mentor junior researchers and contribute to patent development
Qualifications
- PhD in Quantum Computing, Physics, Computer Science, or related field
- 3+ years of experience in quantum algorithm development and implementation
- Proficiency with quantum programming frameworks (Qiskit, Cirq, or Q#)
- Strong background in machine learning, linear algebra, and quantum mechanics
- Experience with high-performance computing and parallel processing systems
- Demonstrated track record of publishing in quantum computing or AI research
- Expertise in Python, TensorFlow, and PyTorch
- Ability to work in fast-paced, innovative environments with minimal supervision