Job Description
Join Nexus Innovations at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We seek a visionary Quantum Computing Research Lead to drive breakthroughs in quantum algorithms, error correction, and hardware optimization. This role offers unparalleled opportunity to shape the next generation of computational technology while collaborating with Nobel laureates and industry pioneers.
Our state-of-the-art facility in San Francisco provides cutting-edge resources and a culture of relentless innovation. You'll lead a multidisciplinary team of physicists, engineers, and data scientists to overcome quantum decoherence challenges and develop commercially viable quantum systems. The ideal candidate combines deep technical expertise with strategic vision to translate theoretical quantum mechanics into real-world applications.
Responsibilities
- Design and execute quantum computing research roadmaps targeting 2026 commercialization milestones
- Lead development of novel quantum algorithms for optimization and simulation problems
- Collaborate with hardware teams to improve qubit stability and error correction protocols
- Publish breakthrough research in top-tier journals and industry whitepapers
- Secure strategic partnerships with quantum hardware providers and research institutions
- Mentor PhD-level researchers and foster cross-functional innovation
- Translate quantum computing concepts into business applications for finance, logistics, and pharmaceuticals
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 8+ years experience
- Proven track record in quantum algorithm development and quantum error correction
- Expertise in quantum programming languages (Q#, Cirq, Quil) and simulators
- Published research in Nature/Science or equivalent top-tier journals
- Experience leading multi-disciplinary technical teams in R&D environments
- Deep understanding of quantum hardware architectures (superconducting, trapped ion, photonic)
- Strong background in machine learning and classical computing optimization