Job Description
Join Nexus Labs at the forefront of technological innovation as we pioneer quantum computing solutions that will redefine 2026 and beyond. We're seeking a visionary Quantum Computing Research Scientist to develop next-generation algorithms and systems that solve previously unsolvable problems. You'll collaborate with world-class physicists, engineers, and data scientists in our state-of-the-art Austin facility, contributing to breakthroughs in cryptography, materials science, and artificial intelligence.
This role offers unparalleled opportunities to shape the quantum revolution while working with cutting-edge hardware from industry partners like IBM, Google, and Rigetti. Your work will directly impact our mission to accelerate scientific discovery and commercial applications of quantum technology by 2026.
Responsibilities
- Design and implement novel quantum algorithms for optimization and simulation problems
- Develop error-correction techniques to advance quantum system stability
- Collaborate with hardware teams to translate theoretical models into practical implementations
- Lead research initiatives focused on quantum machine learning applications
- Publish findings in leading scientific journals and present at international conferences
- Secure federal and private research grants for quantum computing projects
- Mentor junior researchers and contribute to lab's intellectual property portfolio
Qualifications
- PhD in Physics, Computer Science, Mathematics, or related field (or equivalent experience)
- 3+ years of hands-on quantum computing research experience
- Expertise in quantum programming languages (Qiskit, Cirq, or Q#)
- Strong background in quantum information theory and many-body systems
- Proven track record of peer-reviewed publications in quantum computing
- Familiarity with quantum hardware architectures (superconducting, trapped ion, photonic)
- Demonstrated ability to secure research funding through grants and proposals
- Excellent communication skills for technical and non-technical audiences