Job Description
Join Nexus Quantum Dynamics at the forefront of technological revolution in 2026. We're seeking a visionary Quantum Computing Research Scientist to pioneer breakthroughs that will redefine humanity's computational future. In this role, you'll develop next-gen quantum algorithms, collaborate with Nobel laureates, and shape the blueprint for post-classical computing. Our state-of-the-art lab in San Francisco offers unparalleled resources to explore quantum supremacy, cryptography, and machine learning applications. This is your chance to solve problems once deemed impossible while contributing to technologies that will impact global industries.
We offer competitive equity packages, flexible research schedules, and continuous learning opportunities through our partnership with MIT's Quantum Engineering Center. Your work will directly influence projects spanning drug discovery, climate modeling, and artificial intelligence. If you're driven to push the boundaries of what's computationally possible, apply today to become part of quantum history.
Responsibilities
- Design and implement novel quantum algorithms for optimization and simulation problems
- Lead experimental research in quantum error correction and fault-tolerant systems
- Develop quantum machine learning models for predictive analytics and pattern recognition
- Collaborate with hardware teams to optimize quantum software for next-generation processors
- Publish breakthrough research in Nature/Science journals and present at major quantum conferences
- Mentor junior researchers and contribute to quantum education initiatives
- Secure patents and intellectual property for proprietary quantum methodologies
Qualifications
- PhD in Quantum Physics, Computer Science, or Mathematics (post-doc preferred)
- 3+ years of hands-on quantum algorithm development experience
- Expertise in quantum programming languages (Q#, Qiskit, Cirq) and simulation frameworks
- Published research in quantum computing or related fields (top-tier journals)
- Proficiency in Python/C++ and classical machine learning frameworks (TensorFlow, PyTorch)
- Strong background in linear algebra, statistical mechanics, and information theory
- Demonstrated ability to work with cryogenic quantum systems and superconducting qubits