Job Description
Join Nexus Dynamics at the forefront of technological evolution as we pioneer quantum-AI hybrid systems for 2026 and beyond. We're seeking a visionary Quantum AI Research Engineer to architect next-gen computational paradigms that will redefine industries. In this role, you'll collaborate with Nobel laureates and industry disruptors in our state-of-the-art San Francisco lab, developing algorithms that merge quantum supremacy with machine learning intelligence.
About Nexus Dynamics: A Silicon Valley powerhouse with $2B+ in R&D funding, we're accelerating the 2026 technological singularity through quantum computing, neural interfaces, and autonomous systems. Our team holds 300+ patents and partners with NASA, IBM Quantum, and MIT.
What You'll Achieve: Design fault-tolerant quantum neural networks, optimize AI training on quantum processors, and publish breakthrough research in Nature/Science journals. You'll directly influence the roadmap for quantum-AI integration that will power autonomous vehicles, drug discovery, and climate modeling.
Responsibilities
- Architect hybrid quantum-AI algorithms for computational acceleration
- Develop error-corrected quantum neural networks using Qiskit/PennyLane
- Optimize machine learning pipelines on quantum processors
- Lead cross-disciplinary research with quantum physics and AI teams
- Secure $500K+ in government/industry grants for 2026 initiatives
- Author 3+ peer-reviewed publications annually in top-tier journals
- Mentor PhD interns in quantum machine learning methodologies
- Collaborate with hardware teams to co-design quantum-AI hardware
Qualifications
- PhD in Quantum Computing, Machine Learning, or Physics (or equivalent experience)
- Expertise in quantum algorithms (Shor's, Grover's, VQE) and error correction
- Proficiency in Python/C++ with quantum frameworks (Qiskit, Cirq, TensorFlow Quantum)
- Published research in quantum-AI integration (Nature/Science/IEEE QED preferred)
- Experience with high-performance computing clusters and GPU acceleration
- Deep understanding of quantum mechanics principles and tensor networks
- Track record of securing competitive research grants
- Strong background in reinforcement learning or generative adversarial networks