Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution as we pioneer the convergence of quantum computing and artificial intelligence. We're seeking visionary Quantum AI Research Engineers to develop next-generation algorithms that will redefine computational boundaries. Our state-of-the-art facility in San Francisco offers an unparalleled environment for breakthrough innovation, with access to quantum processors, AI superclusters, and a team of Nobel-caliber collaborators. This role represents a rare opportunity to shape the future of human-machine intelligence while contributing to solutions for climate modeling, drug discovery, and sustainable energy systems.
We offer competitive equity packages, flexible work arrangements, and dedicated research budgets for conference participation and patent development. Our culture emphasizes intellectual curiosity, collaborative problem-solving, and measurable impact on global challenges.
Responsibilities
- Design and implement quantum machine learning algorithms leveraging Qiskit and TensorFlow Quantum
- Develop hybrid quantum-classical neural networks for complex pattern recognition tasks
- Optimize quantum circuit architectures for NISQ-era hardware limitations
- Lead cross-functional teams in translating theoretical models into deployable solutions
- Author peer-reviewed publications and contribute to open-source quantum AI frameworks
- Collaborate with hardware teams to co-design quantum processors optimized for AI workloads
- Present research findings at premier conferences like Q2B and NeurIPS
Qualifications
- PhD in Quantum Computing, Machine Learning, or Computational Physics (or equivalent research experience)
- Expertise in quantum algorithms (VQE, QAOA, Grover's variants) and quantum error correction
- Proficiency in Python, C++, and quantum programming frameworks (Qiskit, Cirq, PennyLane)
- Published research in top-tier venues (Nature, Science, arXiv)
- Experience with high-performance computing clusters and GPU-accelerated ML frameworks
- Demonstrated ability to translate complex mathematical concepts into executable code
- Strong background in linear algebra, probability theory, and information theory