Job Description
Join Nexus Quantum Labs at the forefront of technological revolution as we pioneer quantum computing solutions that will redefine 2026 and beyond. As a Quantum Computing Research Scientist, you'll collaborate with Nobel laureates and industry disruptors to solve humanity's most complex challenges. Our state-of-the-art facility in San Francisco offers unparalleled resources for groundbreaking research, competitive compensation, and equity participation in our quantum ecosystem.
We're seeking visionary minds to develop quantum algorithms, optimize qubit stability, and build error-corrected systems. This role offers direct impact on projects spanning drug discovery, climate modeling, and AI acceleration. Your work will shape the technological landscape of the next decade while enjoying San Francisco's vibrant innovation culture.
Responsibilities
- Design and implement novel quantum algorithms for practical applications in materials science and cryptography
- Lead research initiatives to overcome quantum decoherence and error correction challenges
- Collaborate with hardware teams to optimize qubit architectures and gate operations
- Develop hybrid quantum-classical machine learning frameworks for 2026-era AI systems
- Publish breakthrough research in top-tier journals and present at international conferences
- Secure $5M+ in research grants from NSF, DARPA, and private quantum initiatives
- Mentor postdoctoral researchers and drive quantum computing curriculum development
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years of research experience
- Published peer-reviewed work on quantum algorithms or quantum error correction
- Expertise in quantum programming languages (Q#, Qiskit, Cirq) and simulation frameworks
- Deep understanding of quantum mechanics principles and quantum information theory
- Proven track record of securing federal research grants or industry partnerships
- Experience with superconducting qubits, trapped ions, or photonic quantum systems
- Strong background in machine learning and classical HPC optimization techniques