Job Description
Join Nexus Quantum Labs, a pioneer in next-generation AI, as we redefine computing boundaries. We're seeking a visionary Quantum Machine Learning Engineer to architect hybrid quantum-classical systems that will power the 2026 technological revolution. This role sits at the intersection of quantum physics and advanced AI, where you'll transform theoretical possibilities into breakthrough applications.
Our team operates at the bleeding edge of innovation, with access to state-of-the-art quantum hardware and unparalleled R&D resources. You'll collaborate with Nobel-caliber researchers to solve humanity's most complex challenges while enjoying Silicon Valley's premier compensation package and benefits.
Responsibilities
- Design and implement quantum-enhanced machine learning algorithms for real-world applications
- Develop hybrid quantum-classical neural networks achieving exponential computational advantages
- Optimize quantum circuits for error correction and scalability in production environments
- Lead cross-functional teams translating quantum research into deployable AI solutions
- Architect quantum data processing pipelines handling petabyte-scale datasets
- Contribute to open-source quantum ML frameworks and publish breakthrough research
- Mentor junior engineers in quantum computing best practices and emerging protocols
Qualifications
- PhD in Quantum Computing, Machine Learning, or related field (or equivalent experience)
- Expertise in quantum programming frameworks (Qiskit, Cirq, or PennyLane)
- Proven track record implementing production-level ML systems with 10M+ parameter models
- Deep understanding of quantum algorithms (QAOA, VQE, Grover's variants)
- Proficiency in Python/C++ with optimization for quantum hardware constraints
- Experience with cloud quantum platforms (IBM Quantum, Amazon Braket)
- Published research in top-tier quantum ML conferences (QIP, NeurIPS)
- Strong background in linear algebra, probability, and computational complexity