Director, World Model & Agentic Learning

Role: Lead the AI science team developing enterprise world model and agentic learning capabilities within Johnson & Johnson's Generative AI organization. Objectives include designing systems for knowledge accumulation, reasoning, self-awareness of system boundaries, continuous improvement, and knowledge transfer across workflows. Responsibilities encompass setting technical strategy, building mechanisms for representation, confidence, and reasoning, partnering with scientific experts, and leading a team of 4–8 AI scientists. Key focus areas are knowledge representation, agentic learning, and system accountability, emphasizing real-world learning from operational signals rather than retraining. Qualifications include 8+ years of experience in AI/ML system development, expertise with large language models, knowledge graphs, continual learning, and leadership skills. Preferred background entails a PhD in related disciplines, experience in regulated environments like life sciences or healthcare, and contributions to knowledge representation or human-in-the-loop AI. High-value specifics involve applying AI to scientific domains such as drug discovery, utilizing structured knowledge, and ensuring traceability and auditability of decisions. The role reports to the Head of Generative AI, collaborates with data, platform, and scientific teams, and is based at one of several U.S. locations with hybrid work options.