Director, World Model & Agentic Learning

Role: Lead the design and development of enterprise world models and agentic learning capabilities within Johnson & Johnson's Generative AI organization. Objectives: build systems that represent and reason on accumulated domain knowledge, improve from real-world operation without retraining, and support scalable, authoritative AI decision-making in healthcare R&D. Responsibilities: oversee AI team of 4-8 scientists; define technical strategies for knowledge representation, confidence estimation, and continual learning; partner with domain experts and external collaborators; ensure system transparency, auditability, and decision traceability; collaborate with evaluation and platform teams. Qualifications: minimum 8 years of industry experience in AI/ML, with deep hands-on expertise in large language models, retrieval-augmented generation, agentic systems, and knowledge representation; proven track record in designing knowledge-centric, learning-from-operation systems; strong leadership skills. Preferred: advanced degree (PhD), experience in regulated/high-stakes environments (e.g., life sciences, healthcare), background in knowledge graphs or structured memory, and publications in relevant fields. HighValue: focus on knowledge accumulation, continual learning, and reasoning in AI, with applications in pharmaceutical R&D; cross-functional collaboration with scientific and technical teams; hybrid work locations in the U.S.