Job Description
Join Nexus Quantum Systems at the forefront of 2026's technological revolution as we pioneer the convergence of quantum computing and artificial intelligence. We seek a visionary Quantum Machine Learning Engineer to architect next-gen algorithms that solve previously unsolvable problems in cryptography, drug discovery, and climate modeling. Our state-of-the-art lab in San Francisco offers unprecedented access to 1000+ qubit processors and a collaborative environment where your innovations will directly shape humanity's digital future.
Your Impact: You'll bridge quantum hardware and classical ML frameworks, developing hybrid systems that outperform traditional computing by orders of magnitude. This role offers equity participation and the opportunity to publish breakthrough research in partnership with MIT and Stanford.
Responsibilities
- Design and implement quantum neural networks for high-dimensional optimization problems
- Develop error-corrected quantum circuits for real-time ML model training
- Create hybrid quantum-classical pipelines for drug discovery simulations
- Optimize quantum algorithms for fault-tolerant architectures (2026+ hardware)
- Publish 2+ peer-reviewed papers annually on quantum ML advancements
- Mentor cross-functional teams on quantum computing principles
Qualifications
- PhD in Quantum Computing, Physics, or ML with 3+ years industry experience
- Proficiency in Qiskit, Cirq, and quantum circuit optimization
- Published research in Nature/Science on quantum machine learning
- Expertise in tensor networks and quantum information theory
- Experience with quantum annealing for combinatorial optimization
- Fluency in Python/C++ with quantum simulation frameworks