Job Description
Join Nexus Labs at the forefront of 2026's technological revolution as a Quantum Computing Research Scientist. We're pioneering breakthroughs in quantum algorithms and error correction that will redefine computational boundaries. In this role, you'll collaborate with Nobel laureates and AI visionaries to develop scalable quantum systems that solve previously unsolvable problems—from drug discovery to climate modeling. Our state-of-the-art lab in San Francisco offers cutting-edge resources including 512-qubit processors and a dedicated quantum networking infrastructure.
As a key innovator in our Quantum AI Division, you'll contribute to projects that bridge quantum mechanics and machine learning, pushing the boundaries of what's computationally possible. We offer competitive equity packages, flexible hybrid work arrangements, and continuous learning opportunities through our Quantum Futures Academy. Help us build the computational backbone of tomorrow.
Responsibilities
- Design and implement novel quantum algorithms for optimization and simulation problems
- Develop error correction protocols for fault-tolerant quantum systems
- Collaborate with AI teams to integrate quantum processors into hybrid computing frameworks
- Lead research on quantum machine learning applications for real-world data sets
- Publish findings in leading journals and present at international conferences
- Mentor junior researchers and contribute to patent development
- Optimize quantum circuits for current-generation 256+ qubit processors
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years research experience
- Expertise in quantum algorithm design (e.g., Shor's, Grover's, VQE)
- Proficiency with quantum programming languages (Q#, Qiskit, Cirq)
- Strong background in quantum error correction and fault tolerance
- Publication record in top-tier quantum computing journals
- Experience with quantum hardware platforms (Rigetti, IBM Quantum, IonQ)
- Demonstrated ability to translate theoretical concepts into practical implementations
- Collaborative mindset with experience in cross-disciplinary research environments