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 mechanisms that turn operation into improvement, such as active learning from expert corrections, memory-based / in-context learning, or outcome-driven refinement.
• Accumulate, don’t re-derive: agents build on prior understanding instead of re-reading every source, dataset, and prior result on each task.
• Know its own boundaries: the system can say what it knows, what it doesn’t, and how confident it is.
• Reason consistently: expert judgment is applied uniformly across thousands of cases, not improvised per query.
• Improve from operation, not retraining: every run, every expert correction, and every decision outcome makes the next result better.
• Compound across workflows: knowledge earned in one domain or workflow surfaces automatically wherever else it is relevant.
• Keep experts authoritative: experts own the judgment; the system does the maintenance, never the reverse.
• Stay fresh and honest: contradictions, gaps, and staleness are surfaced, never silently buried.
• Be auditable and accountable: every conclusion is traceable, decisions can be reconstructed and judged against their outcomes, and institutional understanding survives turnover.

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.