Job Description
Join Nexus Labs at the forefront of technological evolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking a visionary Quantum Computing Research Scientist to develop breakthrough algorithms and protocols that will redefine computational boundaries. Collaborate with Nobel laureates and industry disruptors in our state-of-the-art Austin facility, equipped with next-generation quantum processors. This role offers unparalleled opportunities to shape the future of artificial intelligence, cryptography, and molecular modeling.
Our competitive compensation package includes equity grants, comprehensive benefits, and dedicated R&D funding. Experience the thrill of solving humanity's most complex challenges while advancing your career in one of America's most innovative tech ecosystems.
Responsibilities
- Design and implement novel quantum algorithms for optimization and simulation problems
- Lead cross-functional research teams to develop fault-tolerant quantum computing protocols
- Publish groundbreaking research in peer-reviewed journals and industry conferences
- Collaborate with hardware engineers to optimize quantum circuit designs
- Develop quantum machine learning frameworks for 2026-era AI applications
- Secure patents for proprietary quantum computing methodologies
- Mentor junior researchers and contribute to technical roadmaps
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 5+ years research experience
- Proven expertise in quantum algorithm development (Shor's, Grover's, VQE)
- Proficiency with quantum programming frameworks (Qiskit, Cirq, Q#)
- Strong publication record in top-tier journals (Nature, Science, IEEE)
- Experience with quantum error correction and fault-tolerance techniques
- Demonstrated ability to translate theoretical concepts into practical implementations
- Excellent communication skills for technical and non-technical audiences
- Preference for candidates with experience in quantum cryptography or quantum machine learning