Role Overview
We are looking for an accomplished Neuro-Symbolic AI Manager to lead advanced research and product initiatives involving hybrid AI systems that combine machine learning with symbolic reasoning. The role will focus on designing innovative AI algorithms, leading multidisciplinary teams, and taking research concepts through scalable production deployment.
Key Responsibilities
Define and drive the roadmap for Neuro-Symbolic AI, reasoning, and hybrid AI systems.
Lead AI initiatives from research and algorithm design through production deployment.
Architect and optimize neuro-symbolic AI models for large-scale, real-world applications.
Develop hybrid architectures combining deep learning, symbolic reasoning, knowledge representation, and probabilistic logic.
Lead and mentor AI researchers, ML engineers, and data scientists.
Collaborate with research teams, academic institutions, and technology partners.
Drive technical innovation and contribute to research publications, patents, and advanced AI initiatives.
Ensure AI solutions are scalable, explainable, secure, and production-ready.
Required Skills & Experience
15+ years of experience in AI research, machine learning, software engineering, or applied AI.
PhD required in Computer Science, Artificial Intelligence, Data Science, or a closely related discipline.
Advanced Python and Go programming experience.
Strong hands-on expertise with PyTorch, PyG (PyTorch Geometric), and PyKEEN.
Deep understanding of Neuro-Symbolic AI, symbolic reasoning, knowledge representation, and hybrid AI architectures.
Strong experience with Graph Neural Networks (GNNs), knowledge graphs, probabilistic logic, and automated reasoning.
Experience with Explainable AI (XAI) and interpretable AI systems.
Hands-on experience deploying AI/ML systems on AWS or Azure.
Experience designing scalable microservices architectures for AI/ML solutions.
Strong leadership experience managing or mentoring multidisciplinary AI/ML teams.
Preferred Background
Experience with AI research labs, hyperscale technology companies, or advanced AI organizations.
Experience in healthcare, financial services, retail, robotics, autonomous systems, or other knowledge-intensive domains.
Experience with reinforcement learning, multimodal AI, LLMs, AI agents, and knowledge graphs.
Experience taking research prototypes into enterprise-scale production environments.
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