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 brilliant Quantum Computing Research Scientist to develop groundbreaking algorithms and protocols that will redefine computational boundaries. In this role, you'll collaborate with Nobel Prize-winning physicists and industry disruptors to solve humanity's most complex challenges in cryptography, materials science, and AI optimization.
Our Austin-based innovation hub offers unparalleled resources including 128-qubit quantum processors and a $50M annual R&D budget. You'll lead projects that directly contribute to our mission of creating quantum-safe infrastructure for critical global systems. This is your opportunity to shape the technological landscape of the next decade while working alongside the brightest minds in quantum physics and computer science.
Responsibilities
- Design and implement novel quantum algorithms for optimization and simulation problems
- Lead research in quantum error correction and fault-tolerant computing architectures
- Collaborate with hardware teams to develop quantum-classical hybrid computing frameworks
- Publish breakthrough research in top-tier quantum computing journals and conferences
- Mentor junior researchers and establish new industry standards for quantum protocols
- Develop quantum-resistant cryptographic solutions for enterprise applications
- Secure patents for innovative quantum computing methodologies and applications
Qualifications
- PhD in Quantum Computing, Theoretical Physics, or Computer Science (or equivalent experience)
- 5+ years of hands-on experience with quantum programming languages (Q#, Qiskit, Cirq)
- Proven track record of peer-reviewed publications in quantum information science
- Expertise in quantum circuit design and quantum error correction techniques
- Deep understanding of quantum mechanics principles and quantum computing architectures
- Experience with high-performance computing and parallel processing environments
- Strong background in machine learning and classical optimization algorithms