Director, World Model & Agentic Learning
About the role
Johnson & Johnson Innovative Medicine is recruiting a Director, World Model & Agentic Learning to join our Data, Data Science & AI organization. This is a newly created leadership role within the Generative AI organization, reporting directly to the Head of Generative AI.
Responsibilities
World Model: design how agents represent and reason against accumulated domain understanding, instead of re-deriving knowledge from raw sources on each task.
Agentic Learning: design the mechanisms that turn operation into improvement, such as active learning from expert corrections, memory-based / in-context learning, or outcome-driven refinement.
Partner with scientists and domain experts to ensure their expertise becomes something the system can apply consistently at scale, keeping experts authoritative.
Define and prove the accountability bar: demonstrate that the system produces better decisions over time, making every conclusion auditable and reconstructable, and judging decisions against their real-world outcomes.
Recruit, build, and lead a team of 4–8 AI scientists, attracting, developing, and retaining top talent in continual learning, knowledge representation, and agentic systems.
Requirements
Minimum 8 years of post-academic industry experience building and shipping AI/ML systems, with significant time owning technical architecture.
Deep, hands-on expertise with modern AI systems: large language models, retrieval-augmented generation, agentic frameworks, and knowledge representation.
Demonstrated track record designing systems where knowledge accumulation, memory, or continual learning was the central technical challenge.
Experience designing systems that learn and improve from real-world operation and expert feedback (e.g., active learning, in-context / memory-based learning, outcome-driven refinement).
Strong people leadership experience, including recruiting, building, and leading technical or scientific teams in a matrixed organization.
Ability to set and defend a technical architecture and hold a team accountable to it.
Excellent communication skills: able to align scientists, engineers, domain experts, and senior stakeholders around a technical strategy.
Qualifications
Advanced degree (PhD preferred) in computer science, AI/ML, applied mathematics, computational science, or a related discipline.
Experience working at the intersection of AI and domain experts in regulated or high-stakes environments (e.g., life sciences, healthcare, finance).
Background in life sciences, drug discovery, or pharmaceutical R&D, or a demonstrated ability to ramp quickly in a scientific domain.
Experience working with knowledge graphs, ontologies, structured memory, or other explicit knowledge representations.
Track record of building auditable, traceable AI systems where decisions must be reconstructed and defended.
Publishations or recognized contributions in continual learning, agentic systems, knowledge representation, or human-in-the-loop AI.
Experience partnering with enterprise platform and IT delivery organizations.
Experience building reusable frameworks or platform capabilities that other teams customize and extend at scale.
Experience defining clean interfaces between a knowledge / memory substrate and reasoning or agent systems.