Job Description
Join Nexus Labs at the forefront of technological revolution as we pioneer quantum computing applications for 2026 and beyond. We seek a visionary Quantum Computing Research Scientist to develop groundbreaking solutions that will redefine computational capabilities. This role offers unparalleled opportunities to shape the future of technology in our state-of-the-art Austin facility, collaborating with Nobel laureates and industry disruptors.
Our team operates at the intersection of physics, computer science, and AI, pushing the boundaries of quantum supremacy. You'll work on projects ranging from quantum cryptography optimization to molecular simulation for drug discovery, all while contributing to our open-source quantum framework. The position includes competitive equity packages and access to our quantum annealing lab with 500+ qubit systems.
Responsibilities
- Design and implement quantum algorithms for optimization and machine learning applications
- Lead cross-functional teams in developing quantum-classical hybrid computing solutions
- Publish breakthrough research in peer-reviewed journals and industry conferences
- Collaborate with hardware engineers to mitigate quantum decoherence challenges
- Develop error-correction protocols for fault-tolerant quantum computing systems
- Secure federal grants and corporate partnerships for quantum research initiatives
- Mentor junior researchers in quantum information theory and experimental physics
Qualifications
- PhD in Physics, Computer Science, or related field with quantum focus
- 3+ years experience with quantum programming frameworks (Qiskit, Cirq, or Q#)
- Expertise in quantum error correction and fault-tolerant architectures
- Published research in quantum computing or quantum information theory
- Proficiency in high-performance computing and parallel processing
- Demonstrated ability to translate theoretical models into practical implementations
- Experience securing research funding from NSF, DARPA, or equivalent institutions
- Strong background in machine learning and classical optimization algorithms