Job Description
Shape the future at Nexus Quantum Dynamics! We're pioneering the convergence of quantum computing and artificial intelligence to solve humanity's greatest challenges by 2026. Join our elite team of researchers developing next-generation quantum neural networks and algorithmic breakthroughs. This is your opportunity to work with industry-leading quantum hardware and AI frameworks in a state-of-the-art facility in San Francisco's tech corridor.
Our ideal candidate thrives at the intersection of theoretical physics and machine learning, pushing the boundaries of computational possibility. You'll collaborate with Nobel laureates and AI pioneers while contributing to projects that will redefine industries from healthcare to climate modeling. We offer competitive equity packages, flexible work arrangements, and dedicated R&D budgets for conference participation and patent development.
Responsibilities
- Design and implement quantum-enhanced machine learning algorithms for real-world applications
- Develop hybrid quantum-classical computing frameworks for complex optimization problems
- Lead research initiatives in quantum neural network architectures and training methodologies
- Collaborate with hardware teams to optimize quantum algorithms for specific qubit technologies
- Author peer-reviewed publications and patents in quantum AI and computational complexity
- Mentor junior researchers and drive cross-functional innovation workshops
Qualifications
- PhD in Quantum Computing, Machine Learning, Physics, or Computer Science (or equivalent experience)
- Expertise in quantum programming languages (Qiskit, Cirq, Q#) and quantum circuit design
- Proven track record of developing novel AI/ML algorithms with published research
- Strong foundation in linear algebra, quantum mechanics, and computational theory
- Experience with high-performance computing frameworks (TensorFlow, PyTorch, CUDA)
- Demonstrated ability to translate complex theoretical concepts into practical implementations
- Passion for solving interdisciplinary problems at the quantum-AI frontier