Director, World Model & Agentic Learning

Job description

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 MedTech, 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. Learn more at jnj.com 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. Job Function: Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: People Leader All Job Posting Locations: 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 Job Description: 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 MedTech, 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. 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, retrieva