Job Description
Join Nexus Quantum Systems at the forefront of computational revolution! We're pioneering quantum-AI convergence solutions that will redefine industries by 2026. As a Quantum Machine Learning Engineer, you'll architect hybrid quantum-classical algorithms, optimize quantum neural networks, and collaborate with Nobel laureates to solve previously unsolvable problems. Our state-of-the-art labs in San Francisco offer unparalleled resources to transform theoretical breakthroughs into real-world applications.
Why Nexus? We provide cutting-edge hardware access, competitive equity packages, and flexible hybrid work arrangements. Our team includes pioneers from Google AI, IBM Quantum, and MIT who are committed to democratizing quantum machine learning.
Responsibilities
- Design and implement quantum-enhanced ML models for optimization, simulation, and pattern recognition
- Develop hybrid quantum-classical frameworks for large-scale data processing
- Collaborate with quantum hardware teams to optimize algorithm performance on real quantum processors
- Create proprietary quantum neural network architectures for enterprise applications
- Lead research initiatives in quantum error correction and fault-tolerant ML
- Mentor junior engineers and publish breakthrough research in top-tier journals
- Drive product roadmap for quantum-AI SaaS solutions targeting 2026 market readiness
Qualifications
- PhD in Quantum Computing, Machine Learning, or related field (MS with 5+ years experience)
- Proficiency in quantum programming languages (Qiskit, Cirq, Q#) and ML frameworks (PyTorch, TensorFlow)
- Published research in quantum machine learning or quantum information theory
- Experience with quantum hardware (IBM Quantum, Rigetti, IonQ) and error mitigation techniques
- Strong background in linear algebra, probability theory, and computational complexity
- Track record of developing production-ready ML systems with 10M+ user impact
- Ability to translate complex quantum concepts for non-technical stakeholders