Job Description
Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking a visionary Quantum Computing Research Scientist to develop groundbreaking algorithms and architectures that will redefine computational boundaries. This role offers unparalleled opportunity to work with state-of-the-art quantum hardware and collaborate with Nobel laureates in our Austin innovation hub.
You'll lead breakthrough research in quantum error correction, quantum machine learning, and cryptographic applications while publishing in top-tier journals and contributing to open-source quantum frameworks. Our team operates at the intersection of physics, computer science, and engineering, with resources including a 128-qubit quantum processor and dedicated supercomputing clusters.
Nexus Labs offers competitive compensation, equity packages, flexible work arrangements, and dedicated R&D budget for conference attendance and equipment acquisition. Shape the future of computing with us.
Responsibilities
- Design and implement novel quantum algorithms for optimization and simulation problems
- Develop quantum error correction protocols for fault-tolerant computing systems
- Lead research initiatives in quantum machine learning applications
- Collaborate with hardware teams to optimize quantum circuit performance
- Author peer-reviewed publications and contribute to open-source quantum libraries
- Secure external funding through NSF and DARPA grant proposals
- Mentor junior researchers and lead cross-functional quantum projects
Qualifications
- PhD in Quantum Computing, Physics, Computer Science, or related field
- 3+ years of hands-on quantum algorithm development experience
- Proficiency in quantum programming languages (Q#, Qiskit, Cirq)
- Published research in quantum information science or equivalent industry impact
- Expertise in quantum error correction and fault-tolerant architectures
- Demonstrated ability to translate theoretical concepts into practical implementations
- Strong background in linear algebra, probability theory, and computational complexity
- Experience with high-performance computing environments and parallel processing