Job Description
Join Nexus Quantum Dynamics at the forefront of 2026's technological revolution. We're seeking a visionary Quantum Machine Learning Engineer to architect hybrid quantum-classical systems that will redefine artificial intelligence. As a pioneer in quantum supremacy applications, you'll collaborate with Nobel laureates and industry disruptors to develop algorithms that solve previously impossible computational challenges. Our state-of-the-art lab in San Francisco offers unparalleled resources for quantum experimentation, including access to 100+ qubit processors and exclusive partnerships with quantum hardware manufacturers. This role represents the rare opportunity to shape the next decade of computational innovation while accelerating your career in one of tech's most explosive growth sectors.
Responsibilities
- Design and implement quantum neural networks for optimization and pattern recognition tasks
- Develop hybrid quantum-classical ML pipelines leveraging 2026's fault-tolerant quantum processors
- Lead research in quantum-enhanced reinforcement learning for autonomous systems
- Architect quantum data preprocessing frameworks for high-dimensional datasets
- Collaborate with quantum hardware teams to optimize algorithm performance on emerging quantum platforms
- Author white papers and patents on quantum machine learning breakthroughs
- Mentor junior researchers in quantum algorithm development best practices
Qualifications
- PhD in Quantum Computing, Physics, or Machine Learning with 3+ years of quantum algorithm development
- Expertise in quantum programming frameworks (Qiskit, Cirq, PennyLane) and classical ML libraries (PyTorch, TensorFlow)
- Published research in quantum machine learning or quantum information theory
- Proven experience with quantum error correction and noise mitigation techniques
- Strong background in linear algebra, quantum mechanics, and computational complexity
- Proficiency in Python/C++ with high-performance computing optimization skills
- Demonstrated ability to translate theoretical concepts into practical implementations