Job Description
Shape the future of technology as a Quantum AI Research Scientist at Nexus Quantum Systems. We're pioneering the intersection of quantum computing and artificial intelligence to solve humanity's greatest challenges. In this cutting-edge role, you'll develop novel algorithms, prototype quantum neural networks, and collaborate with world-class physicists to unlock exponential computational power. Our state-of-the-art lab in San Francisco offers unparalleled resources to transform theoretical breakthroughs into real-world applications.
Join our mission to accelerate the next technological revolution by designing quantum-resistant AI systems and training the next generation of quantum machine learning models. This position includes competitive equity, flexible work arrangements, and a comprehensive benefits package designed for top-tier researchers.
Responsibilities
- Design and implement quantum machine learning algorithms for optimization and pattern recognition
- Develop hybrid quantum-classical neural network architectures for complex data analysis
- Lead experimental validation of quantum AI models on D-Wave and IBM quantum processors
- Collaborate with quantum hardware teams to co-design qubit-efficient AI frameworks
- Publish groundbreaking research in top-tier journals and industry whitepapers
- Mentor junior researchers and contribute to quantum AI curriculum development
- Secure patents for novel quantum AI methodologies and applications
Qualifications
- PhD in Quantum Computing, Machine Learning, or Physics with 3+ years research experience
- Expertise in quantum algorithms (QAOA, VQE, Grover's) and quantum circuit design
- Proficiency in Python with libraries like Qiskit, Cirq, or PennyLane
- Strong publication record in quantum machine learning or adjacent fields
- Experience with high-performance computing and GPU-accelerated frameworks
- Demonstrated ability to translate theoretical concepts into experimental prototypes
- Knowledge of quantum error correction and fault-tolerant computing principles