Job Description
Join Nexus Future Labs at the forefront of technological innovation as we pioneer the convergence of quantum computing and artificial intelligence. We're seeking visionary Quantum AI Research Engineers to develop breakthrough algorithms that will redefine computational capabilities by 2026. Collaborate with Nobel laureates and industry pioneers in our state-of-the-art research facility, where your work will directly impact quantum-resistant cryptography, next-generation AI models, and autonomous systems.
As a key member of our Quantum AI division, you'll access unparalleled resources including quantum annealing hardware, 100+ qubit simulators, and petabyte-scale datasets. Our culture values intellectual curiosity, rapid prototyping, and cross-disciplinary collaboration. We offer competitive equity packages, flexible work arrangements, and dedicated research time for personal innovation projects.
Responsibilities
- Design and implement quantum machine learning algorithms for optimization problems
- Develop hybrid quantum-classical AI frameworks for real-world applications
- Lead research initiatives in quantum neural networks and quantum-enhanced NLP
- Create fault-tolerant quantum computing protocols for large-scale AI training
- Collaborate with hardware teams to co-design quantum-AI integrated systems
- Publish breakthrough research in top-tier journals and conferences
- Secure patents for novel quantum-AI methodologies and architectures
Qualifications
- PhD in Quantum Computing, Machine Learning, or related field with 3+ years industry experience
- Expertise in quantum algorithms (QAOA, VQE, QML frameworks)
- Proficiency in quantum programming languages (Qiskit, Cirq, Q#)
- Strong background in deep learning frameworks (PyTorch, TensorFlow)
- Published research in quantum computing or AI at NeurIPS/ICML/QIP
- Experience with high-performance computing and distributed systems
- Understanding of quantum error correction and fault tolerance techniques
- Track record of translating theoretical research into practical implementations