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.

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.

The role carries two co-equal mandates:

• 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.

What We Need the System to Do

• 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.

Key Responsibilities

World Model

• 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.

Agentic Learning

• Design the mechanisms that turn operation into improvement. For example, active learning from expert corrections, memory-based / in-context learning, or outcome-driven refinement.
• Make every run, expert correction, and decision outcome a signal that improves the next result.
• Keep institutional understanding fresh and honest as sources, evidence, and experts change over time.

Expert Partnership

• Partner with scientists and domain experts so their expertise becomes something the system can apply consistently at scale.
• Keep experts authoritative: the system maintains and applies their judgment; it never overrides it.

Accountability & Evaluation

• Define and prove the accountability bar: demonstrate that the system produces better decisions over time.
• Make every conclusion auditable and reconstructable, and judge decisions against their real-world outcomes.
• Partner with the J&J Technology, Generative AI evaluation, and the AI operations teams, consuming their per-decision outcome signals as the learning signal and validating decision-quality improvement rigorously.

Team Leadership

• Recruit, build, and lead a team of 4–8 AI scientists.
• Attract, develop, and retain top talent in continual learning, knowledge representation, and agentic systems.
• Establish a culture of scientific rigor, ownership, and accountability within the team.

What This Role Is Not

• Not a generation-first role: the hard problem here is knowledge accumulation and learning over time, not content generation — though the system uses generative models throughout.
• Not platform or application engineering: the Generative AI Platform team owns the R&D agentic platform and its deployment surfaces.
• Not evaluation governance: the Generative AI evaluation function owns independent evaluation; this role partners with it.
• Not the data or memory substrate: the platform’s governed data and context/memory layers manage data and orchestration. This role references and builds on them — it does not rebuild pipelines or own the memory plumbing.

You Might Be Right If

• You’ve built systems where knowledge accumulation and continual learning were the hard problem, not generation.
• You think about large language models as reasoning engines that need structured knowledge to reason against — and structured feedback to improve from.
• You’ve designed learning loops that don’t depend on retraining: active learning from expert corrections, memory-based / in-context learning, outcome-driven refinement.
• You believe the right test of an AI system is the quality of decisions it produces over time — and that those decisions are themselves the signal it learns from.
• You’ve worked at the intersection of AI and domain experts in regulated or high-stakes environments.
• You can hold the architecture in your head and the team accountable to it.

Key Qualifications

• 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.

Preferred 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.
• Publications 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.

Key Relationships

This role is highly collaborative and partners across J&J’s technology, data, and scientific organizations:

• Head of Generative AI — direct manager; sets organizational direction and priorities.
• Generative AI Platform team — owns the R&D agentic platform on which this capability runs.
• Generative AI Evaluation & Standards function — provides independent evaluation and per-decision outcome signals.
• Johnson & Johnson Technology (JJT) — enterprise technology, infrastructure, and engineering delivery partnership.
• Data Strategy & Products (DS&P) — data foundations, governed data, and platform partnership.
• Global Regulatory Affairs, Global Development, Therapeutic Areas (TA) and R &D domain teams — scientific and domain experts who customize and apply the capability to their workflows.
• External academic and industry partners — collaborations that advance continual learning, knowledge representation, and agentic systems.