Job Description
Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer breakthroughs in quantum computing for 2026 and beyond. We're seeking a visionary Research Scientist to develop scalable quantum algorithms and error-correction protocols that will redefine computational boundaries. In this role, you'll collaborate with Nobel Prize-winning physicists and lead projects funded by DARPA and NASA to solve previously unsolvable problems in materials science, cryptography, and artificial intelligence.
Our state-of-the-art facility in San Francisco houses 128-qubit quantum processors operating at millikelvin temperatures. You'll access exclusive resources including our proprietary quantum cloud platform and collaborate with interdisciplinary teams across our global innovation hubs. This position offers unparalleled opportunities to shape the next generation of computational paradigms.
Responsibilities
- Design and implement novel quantum algorithms for optimization and machine learning applications
- Lead research on fault-tolerant quantum computing architectures and error correction methodologies
- Develop quantum-classical hybrid computing frameworks for real-world deployment
- Publish groundbreaking research in Nature/Science journals and present at IEEE Quantum Week
- Mentor PhD researchers and secure $5M+ in government/industry research grants
- Collaborate with hardware teams to co-design quantum processors and control systems
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years research experience
- Expertise in quantum circuit design, quantum information theory, and quantum algorithms
- Published work in top-tier quantum computing journals or conferences
- Proficiency in quantum programming frameworks (Qiskit, Cirq, Q#) and high-performance computing
- Experience with cryogenic quantum systems and superconducting qubit manipulation
- Demonstrated ability to secure federal research funding (NSF, DOE, DARPA)
- Strong background in machine learning and classical optimization techniques