Director, World Model & Agentic Learning

Role: Lead the AI science team within Johnson & Johnson's Generative AI organization, focusing on developing enterprise world models and agentic learning capabilities for R&D applications. Objectives include designing systems that represent and reason over accumulated domain knowledge, improve from operational use through expert feedback, and maintain expert authority over judgments. Responsibilities involve defining technical approach, building mechanisms for knowledge representation, confidence assessment, reasoning, and continuous learning, and partnering with scientific and technical stakeholders across regulated environments like healthcare and life sciences. Lead a team of 4–8 AI scientists, foster a culture of scientific rigor, and ensure system accountability and auditability. Qualifications: minimum 8 years post-academic experience in AI/ML, with deep expertise in large language models, knowledge representation, continual learning, and agentic systems. Proven leadership and experience designing knowledge-centric AI systems that learn from real-world operations, especially in high-stakes or regulated sectors. Preferred: advanced degree (PhD), experience in life sciences or drug discovery, familiarity with knowledge graphs and structured memory, and a track record of building auditable AI frameworks. High-Value: focus on knowledge accumulation, continual learning, and reasoning in AI systems, with applications in healthcare R&D. Location: hybrid model across U.S. offices (New Brunswick, Titusville, Spring House, or Cambridge).