Job Description
Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer the next generation of AI-quantum hybrid systems. We're seeking a visionary Quantum AI Integration Specialist to bridge the gap between quantum computing breakthroughs and practical machine learning applications. This role demands a blend of deep technical expertise and innovative problem-solving in one of the world's most advanced R&D environments. You'll collaborate with Nobel Prize-winning physicists and AI industry leaders to develop scalable quantum neural networks that will redefine computational boundaries by 2026.
What you'll achieve: Design and implement quantum-classical hybrid algorithms, optimize quantum machine learning pipelines, and publish breakthrough research in top-tier journals. Your work will directly impact our flagship 'Project Chronos' initiative to create the world's first commercially viable quantum-AI accelerator.
Responsibilities
- Architect quantum-classical hybrid computing architectures for real-world AI applications
- Develop error-corrected quantum neural networks with >99.9% fidelity
- Optimize quantum machine learning algorithms for hardware acceleration
- Lead cross-functional teams of quantum physicists and ML engineers
- Drive R&D for quantum-AI integration frameworks in cloud-native environments
- Secure $5M+ in research grants through innovative proposals
- Mentor junior researchers in quantum computing best practices
Qualifications
- PhD in Quantum Computing, Physics, or Computer Science with 3+ years industry experience
- Published research in quantum machine learning or quantum algorithms
- Expertise in Qiskit, Cirq, or equivalent quantum programming frameworks
- Proficiency with TensorFlow Quantum and PyTorch Quantum
- Experience with superconducting quantum processors or ion trap systems
- Strong background in complex mathematical optimization techniques
- Track record of leading technical projects with measurable outcomes
- Patent or peer-reviewed publication in quantum computing required