Job Description
Join Nexus Future Labs at the forefront of technological innovation as we pioneer the next wave of Quantum AI systems. We're seeking a visionary Quantum AI Research Engineer to develop revolutionary algorithms that will redefine computing by 2026. Collaborate with Nobel Prize-winning physicists and top-tier AI researchers in our state-of-the-art facility overlooking the San Francisco Bay.
This role offers unparalleled opportunities to shape the future of quantum machine learning, working on projects that directly impact global industries from healthcare to climate modeling. You'll have access to our quantum annealing supercomputers and receive comprehensive benefits including equity, wellness stipends, and sabbatical programs.
Responsibilities
- Design and implement hybrid quantum-classical machine learning models for complex optimization problems
- Develop novel error correction protocols for fault-tolerant quantum computing systems
- Create quantum neural network architectures leveraging 2026-era hardware capabilities
- Lead cross-functional teams in translating theoretical quantum algorithms into production-ready solutions
- Author breakthrough research papers for top-tier journals and industry whitepapers
- Mentor junior researchers in quantum information science and AI integration
- Collaborate with hardware teams to co-design next-generation quantum processors
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years industry experience
- Expertise in quantum programming languages (Qiskit, Cirq, Quipper) and quantum circuit design
- Proven track record of publishing in Nature/Science journals or top-tier conferences
- Strong background in machine learning frameworks (PyTorch, TensorFlow) and high-performance computing
- Experience with quantum error correction protocols and fault-tolerant architectures
- Demonstrated ability to lead complex technical projects with cross-disciplinary teams
- Deep understanding of quantum information theory and quantum many-body systems