Job Description
Join the vanguard of technological evolution at Nexus Future Labs. We're pioneering the convergence of quantum computing and artificial intelligence to solve humanity's most complex challenges by 2026. As a Quantum AI Integration Specialist, you'll architect the next generation of hybrid systems that redefine computational boundaries. This role sits at the intersection of theoretical physics, machine learning, and practical deployment, offering unprecedented opportunities to shape our technological future.
Our award-winning team operates in a state-of-the-art facility with access to quantum annealing hardware, GPU-accelerated supercomputing clusters, and a culture that celebrates intellectual courage. We offer competitive equity packages, unlimited learning stipends, and the autonomy to drive breakthrough innovations.
Responsibilities
- Design and implement quantum-classical hybrid algorithms for optimization problems in logistics, cryptography, and drug discovery
- Develop error mitigation protocols for NISQ-era quantum processors while preparing for fault-tolerant systems
- Create machine learning pipelines that leverage quantum-inspired classical algorithms for 2026-era datasets
- Collaborate with hardware teams to co-design quantum AI accelerators and optimize classical-quantum interfaces
- Lead cross-functional projects integrating quantum computing into existing AI frameworks (PyTorch, TensorFlow Quantum)
- Author white papers and patents for novel quantum AI methodologies targeting 2026 deployment milestones
- Mentor junior researchers while maintaining active publication in top-tier quantum/AI journals
Qualifications
- PhD in Quantum Computing, Machine Learning, or Computational Physics (or equivalent experience)
- 3+ years hands-on experience with quantum programming frameworks (Qiskit, Cirq, PennyLane)
- Expertise in quantum error correction and noise-aware algorithm design
- Proven track record deploying production quantum-classical hybrid systems
- Deep understanding of transformer architectures and quantum neural networks
- Experience with cloud quantum platforms (IBM Quantum, Amazon Braket, Azure Quantum)
- Strong background in Python, C++, and high-performance computing
- Publications at conferences like QIP, NeurIPS, or ICML required