Job Description
Join the frontier of human innovation at Nexus Quantum Labs, where we're engineering the quantum computing breakthroughs of 2026. We're seeking a visionary Quantum AI Researcher to pioneer the next generation of hybrid quantum-classical intelligence systems. Our multidisciplinary team operates at the intersection of quantum physics, machine learning, and computational neuroscience to solve problems previously deemed unsolvable.
At our state-of-the-art San Francisco facility, you'll collaborate with Nobel laureates and Turing Award winners to develop proprietary quantum algorithms that will redefine artificial intelligence. We offer competitive compensation, equity packages, and unparalleled resources including access to our 256-qubit quantum annealer network. This is your chance to shape the technological landscape of the coming decade.
Responsibilities
- Design and implement novel quantum machine learning algorithms for pattern recognition optimization
- Develop hybrid quantum-classical neural network architectures for enhanced computational efficiency
- Lead research initiatives in quantum-enhanced natural language processing systems
- Collaborate with hardware engineers to optimize quantum circuit designs for AI applications
- Publish breakthrough findings in peer-reviewed journals and industry whitepapers
- Mentor junior researchers in quantum computing principles and AI methodologies
- Secure research grants and partnerships with leading academic institutions
Qualifications
- PhD in Quantum Computing, Physics, Computer Science, or related field (or equivalent experience)
- 3+ years of hands-on experience with quantum programming frameworks (Qiskit, Cirq, or Q#)
- Deep understanding of quantum algorithms, quantum error correction, and quantum supremacy principles
- Expertise in machine learning frameworks (TensorFlow, PyTorch) with quantum integration experience
- Publication record in top-tier quantum computing or AI conferences/journals
- Proficiency in Python, C++, and quantum circuit optimization techniques
- Strong background in linear algebra, probability theory, and quantum mechanics