Job Description
Join Nexus Labs at the forefront of the 2026 quantum revolution! We're seeking a pioneering Quantum Computing Research Scientist to architect the next generation of computational paradigms. In this transformative role, you'll collaborate with Nobel laureates and industry disruptors to develop quantum algorithms that will redefine cryptography, AI, and materials science. Our state-of-the-art facility in San Francisco offers unparalleled resourcesâincluding 128-qubit processors and cryogenic computing clustersâto accelerate your breakthroughs.
Competitive compensation includes equity, relocation assistance, and a dedicated R&D budget. Shape the future of technology while enjoying California's vibrant tech ecosystem and Nexus Labs' commitment to work-life integration through flexible schedules and sabbatical programs.
Responsibilities
- Design and implement novel quantum algorithms for optimization, simulation, and machine learning applications
- Lead experimental validation of quantum supremacy claims using superconducting and photonic systems
- Develop error correction protocols for fault-tolerant quantum computing architectures
- Collaborate with hardware teams to co-design quantum-classical hybrid computing frameworks
- Publish breakthrough research in Nature/Science journals and present at IEEE Quantum Week
- Mentor postdoctoral researchers and drive quantum literacy initiatives
- Secure $5M+ in DARPA/NSF grants for quantum computing infrastructure development
Qualifications
- PhD in Quantum Physics, Computer Science, or Applied Mathematics (postdoc experience preferred)
- 3+ years of hands-on quantum algorithm development with Qiskit/Cirq frameworks
- Expertise in quantum error correction codes (surface, LDPC, color codes)
- Publication record in top-tier quantum computing conferences (QIP, ACM Quantum)
- Proficiency in Python/C++ with quantum computing libraries (Qiskit, PennyLane)
- Experience with quantum hardware interfaces (Rigetti, IBM Quantum Experience)
- Demonstrated ability to translate theoretical models into experimental implementations