Job Description
Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing applications for 2026 and beyond. We're seeking a visionary Quantum Computing Research Scientist to develop groundbreaking algorithms and protocols that will redefine computational capabilities. You'll collaborate with Nobel laureates and industry disruptors in our state-of-the-art San Francisco facility, where your work directly impacts fields from drug discovery to climate modeling.
As part of our elite Future Technologies Division, you'll leverage our $50M quantum infrastructure to solve previously impossible problems. We offer competitive equity packages, unlimited professional development, and the autonomy to explore emerging paradigms like topological quantum computing and quantum machine learning.
Responsibilities
- Design and implement novel quantum algorithms for optimization and simulation problems
- Lead cross-functional R&D teams to translate quantum theory into practical applications
- Develop error-correction protocols for fault-tolerant quantum systems
- Collaborate with hardware teams to optimize quantum circuit performance
- Publish breakthrough research in top-tier scientific journals and conferences
- Secure and manage multi-million dollar research grants from government and industry partners
- Mentor junior researchers and contribute to quantum computing education initiatives
Qualifications
- PhD in Quantum Physics, Computer Science, or related field (post-doc preferred)
- 3+ years of hands-on experience with quantum programming languages (Q#, Qiskit, Cirq)
- Published research in quantum information theory or quantum algorithms
- Deep understanding of quantum decoherence and error correction methodologies
- Expertise in at least one classical programming language (Python, C++, or Rust)
- Experience with quantum simulators and real quantum hardware (IBM Q, Rigetti, etc.)
- Track record of securing competitive research funding
- Strong background in linear algebra, probability theory, and computational complexity