Director, Model & Agentic Learning

Job Category

People Leader

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented, treated, and cured,where treatments are smarter and less invasive, and solutions are personal.

Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity.

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As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Data Analytics & Computational Sciences

Data Science

People Leader

Cambridge, Massachusetts, United States of America, La Jolla, California, United States of America, Spring House, Pennsylvania, United States of America, Titusville, New Jersey, United States of America

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for humanity.

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

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.

You will lead the AI science team that builds our enterprise world model and agentic-learning capability for the R&D agentic AI platform, a reusable, expert-curated foundation that domain teams customize, together with the mechanisms by which it improves with use. This is a durable, product-agnostic capability. You will devise the approach, set the technical direction, and lead the team that delivers it.

• World Model: how agents represent and reason against accumulated domain understanding, instead of re-deriving everything from raw sources on each task.
• Agentic Learning: how that understanding grows with use, i.e. getting better from operation, rather than from retraining foundational models.

• 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.

• Design how agents represent accumulated domain understanding and reason against it, rather than re-deriving knowledge from raw sources on each task.
• Build mechanisms for the system to represent its own confidence, boundaries, gaps, and contradictions explicitly.
• Ensure knowledge earned in one domain or workflow compounds and surfaces wherever else it is relevant.
• Serve the representation to the reasoning agents as queryable, grounded knowledge with provenance and confidence, and curate what they propose back by validating, deduplicating, and resolving conflicts.
• Build on the platform’s existing context, memory, and governed data layers, referencing canonical entities rather than rebuilding data pipelines.

• Design the mechanisms that turn operation into improvement. For example, active learning from expert corrections, memory-based / in-context learning, or outcome-driven…