Job Description
Join Nexus Quantum Labs at the forefront of computational innovation as we pioneer the next frontier in artificial intelligence. We're seeking a visionary Quantum Machine Learning Engineer to architect and deploy hybrid quantum-classical algorithms that will redefine industry standards by 2026. This role offers unparalleled opportunities to shape the future of AI while working alongside Nobel Prize-winning physicists and Turing Award-winning computer scientists in our state-of-the-art research facility.
Our compensation package includes equity grants, comprehensive health benefits, and a flexible work environment designed to maximize creativity. You'll collaborate with teams developing quantum-resistant encryption, autonomous quantum systems, and next-gen neural networks operating at the intersection of quantum mechanics and deep learning.
Responsibilities
- Design and implement hybrid quantum-classical neural networks for real-world applications
- Develop quantum algorithms for optimization problems in finance, logistics, and materials science
- Create quantum-resistant machine learning pipelines for enterprise clients
- Lead research on quantum error mitigation in deep learning frameworks
- Collaborate with quantum hardware teams to model and optimize qubit interactions
- Publish peer-reviewed research in top-tier quantum computing journals
- Mentor junior researchers in quantum machine learning methodologies
Qualifications
- PhD in Quantum Computing, Machine Learning, or related field (MS with 5+ years experience)
- Expertise in Python, TensorFlow/PyTorch, and quantum computing frameworks (Qiskit, Cirq)
- Published research in quantum machine learning or quantum information theory
- Experience with cloud quantum computing platforms (IBM Quantum, Amazon Braket)
- Strong background in linear algebra, quantum mechanics, and statistical modeling
- Demonstrated ability to translate complex theoretical concepts into practical implementations
- Experience with high-performance computing and distributed systems