Director, World Model & Agentic Learning

Role: Lead the AI science team within Johnson & Johnson's Generative AI organization, focusing on developing enterprise-level world models and agentic learning systems for R&D applications. Responsibilities include designing knowledge representation mechanisms, ensuring systems reason and learn continuously from real-world operations, partnering with scientific experts, and establishing accountability standards. The role involves building a durable, reusable knowledge foundation that enhances decision-making across domains, with a focus on decision accuracy, transparency, and model integrity. Requirements: Minimum 8 years of industry experience in AI/ML, deep expertise in large language models, knowledge representation, continual learning, and systems architecture; proven leadership in building and guiding technical teams. Preferred: PhD in related fields, experience in regulated environments like life sciences or healthcare, and familiarity with knowledge graphs and auditable AI systems. High-Value: Focus on knowledge accumulation, agentic frameworks, and continual learning to support high-stakes scientific work. Stakeholders include scientific and technical teams, regulatory, and enterprise technology partners. Location: Hybrid work in U.S. offices (New Brunswick, Titusville, Spring House, Cambridge). The role emphasizes collaboration across scientific and technical domains, with a strategic and technical leadership focus on innovative AI systems for pharma research.