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: design and implement systems that enable knowledge accumulation, reasoning, self-assessment, and continual improvement over real-world scientific data. Responsibilities: architect mechanisms for structured knowledge representation, confidence estimation, and self-improvement; partner with domain experts to integrate scientific expertise; oversee team recruitment and development; ensure system accountability, traceability, and auditability; collaborate with evaluation and platform teams to validate decision quality. Qualifications: 8+ years in AI/ML, with extensive hands-on experience designing knowledge representation, continual learning, and reasoning systems; proven leadership in building and guiding scientific or technical teams; strong communication skills for cross-disciplinary stakeholder alignment. High-Value: focus on knowledge accumulation, agentic frameworks, structured memory, and continual learning in regulated or high-stakes environments such as life sciences or healthcare R&D; experience with AI system architecture, active learning, and domain-specific knowledge integration preferred. Work setup: hybrid in U.S. offices (Titusville, NJ; Spring House, PA; Cambridge, MA; La Jolla, CA); travel expectations not specified.