Job Description
Join QuantumLeap Dynamics at the forefront of technological evolution as we pioneer AI systems for 2026. We seek an AI Systems Architect to design next-generation neural frameworks that will redefine human-machine collaboration. This role offers unparalleled opportunities to shape the future of autonomous systems, working with our elite team of quantum computing specialists and neuroengineers.
As a key innovator, you'll architect scalable AI ecosystems that integrate seamlessly with emerging technologies like neuromorphic chips and quantum neural networks. You'll lead projects that push the boundaries of machine perception, autonomous decision-making, and ethical AI governance. Our startup environment fosters rapid prototyping and breakthrough thinking, with competitive equity packages and state-of-the-art resources at your disposal.
Responsibilities
- Design and implement hybrid AI architectures combining classical ML with quantum/neuromorphic computing paradigms
- Lead cross-functional teams in developing ethical AI frameworks for 2026 deployment scenarios
- Optimize neural network topologies for real-time processing in edge-to-cloud environments
- Develop adaptive learning systems that evolve with emerging data streams
- Create security protocols for AI systems operating in distributed quantum networks
- Architect human-AI collaborative interfaces using advanced NLP and computer vision
- Research and implement cutting-edge AI techniques including federated learning and explainable AI
Qualifications
- PhD in Computer Science/AI with 5+ years of systems architecture experience
- Expertise in quantum machine learning frameworks and neuromorphic computing
- Proven track record deploying production-grade AI systems at scale
- Proficiency in PyTorch, TensorFlow Quantum, and low-latency optimization
- Deep understanding of ethical AI principles and regulatory compliance (GDPR, EU AI Act)
- Experience with distributed computing on AWS/GCP/Azure quantum platforms
- Strong background in reinforcement learning and generative adversarial networks
- Published research in top-tier AI/ML conferences (NeurIPS, ICML, ICLR)