Job Description
Join Nexus Quantum Labs at the forefront of technological evolution. As we pioneer the next wave of quantum-powered AI in 2026, we seek a visionary Quantum Machine Learning Engineer to architect hybrid quantum-classical systems that will redefine computational boundaries. You'll collaborate with Nobel-caliber researchers to transform theoretical quantum algorithms into breakthrough solutions for climate modeling, drug discovery, and autonomous systems. Our state-of-the-art quantum lab offers unparalleled resources to develop novel neural network architectures leveraging qubit entanglement and quantum coherence phenomena.
This role represents the intersection of physics and AI, where your work will directly contribute to solving humanity's most complex challenges through quantum advantage. We offer competitive equity packages, flexible work arrangements, and access to exclusive industry conferences shaping 2026's tech landscape.
Responsibilities
- Design and implement hybrid quantum-classical machine learning frameworks leveraging quantum processors
- Develop novel quantum neural network architectures for optimization and pattern recognition tasks
- Collaborate with quantum hardware teams to optimize algorithms for current and near-term quantum devices
- Create robust simulation environments for quantum machine learning workflows
- Publish research in top-tier quantum computing and AI conferences/journals
- Lead cross-functional projects integrating quantum solutions with classical AI pipelines
- Mentor junior researchers in quantum algorithm development and quantum software engineering
Qualifications
- PhD in Quantum Computing, Machine Learning, Physics, or Computer Science (MS with exceptional experience considered)
- Proven expertise in quantum programming (Qiskit, Cirq, or Q#) and quantum algorithm design
- Strong background in deep learning frameworks (TensorFlow/PyTorch) with quantum integration experience
- Published research in quantum machine learning or quantum information science
- Proficiency in Python, C++, and high-performance computing environments
- Demonstrated ability to translate complex quantum concepts into practical implementations
- Experience with quantum hardware integration (IBM Quantum, Rigetti, or IonQ platforms)