Job Description
Shape the future of technology in 2026 with Nexus Quantum Dynamics. We're pioneering the convergence of quantum computing and machine learning to solve humanity's most complex challenges. As a Quantum Machine Learning Engineer, you'll architect next-gen AI systems that leverage quantum phenomena for exponential computational power. Join our interdisciplinary team at the forefront of quantum innovation, where your work will directly impact fields from drug discovery to climate modeling.
We offer unparalleled resources including access to quantum annealers, GPU-accelerated cloud platforms, and a collaborative R&D environment. Our commitment to cutting-edge research is matched only by our dedication to your professional growth through mentorship programs and conference participation.
Responsibilities
- Design and implement quantum neural networks for pattern recognition and optimization tasks
- Develop hybrid quantum-classical algorithms leveraging NISQ-era hardware capabilities
- Create quantum-enhanced machine learning pipelines for predictive analytics and data modeling
- Collaborate with physicists to translate quantum phenomena into practical ML applications
- Optimize quantum circuit implementations for error resilience and computational efficiency
- Lead research initiatives in quantum machine learning theory and algorithm development
- Document technical specifications and publish findings in peer-reviewed journals
Qualifications
- PhD in Quantum Computing, Machine Learning, or related computational field
- Expertise in quantum programming languages (Qiskit, Cirq, Q#) and quantum circuit design
- Proven experience with deep learning frameworks (TensorFlow, PyTorch) and high-performance computing
- Strong foundation in linear algebra, quantum mechanics, and information theory
- Published research in quantum machine learning or adjacent domains
- Proficiency in Python, C++, and cloud platforms (AWS/Azure quantum services)
- Demonstrated ability to translate complex quantum concepts into practical implementations