Job Description
Join Nexus Quantum Systems at the forefront of technological evolution as we pioneer the next generation of quantum-AI convergence. We seek visionary researchers to develop transformative solutions in quantum machine learning, cryptography, and computational modeling. Our Austin-based hub offers unparalleled resources and a collaborative environment where your work will directly shape the 2026 technological landscape.
As a key member of our Future Technologies Division, you'll leverage state-of-the-art quantum hardware and advanced neural networks to solve previously insurmountable challenges. We provide competitive compensation, flexible work arrangements, and continuous learning opportunities through our Quantum Academy partnership program.
Responsibilities
- Design and implement quantum algorithms for AI optimization and pattern recognition
- Lead cross-functional teams in developing hybrid quantum-classical computing frameworks
- Conduct pioneering research in quantum neural networks and probabilistic computing
- Collaborate with hardware engineers to optimize quantum system performance
- Publish breakthrough research in peer-reviewed journals and industry conferences
- Develop patentable methodologies for quantum-resistant cryptography solutions
- Mentor junior researchers and contribute to technical strategy roadmaps
Qualifications
- PhD in Quantum Computing, Machine Learning, or related field with 3+ years industry experience
- Proficiency in quantum programming languages (Qiskit, Cirq, or PennyLane)
- Expertise in advanced AI frameworks (PyTorch, TensorFlow) and classical optimization
- Published research in quantum machine learning or quantum information theory
- Strong background in quantum error correction and fault-tolerant systems
- Experience with high-performance computing environments and cloud platforms
- Demonstrated ability to translate complex theoretical concepts into practical applications
- Excellent written and verbal communication skills for technical and non-technical audiences