Director, World Model & Agentic Learning
Johnson & Johnson Innovative Medicine
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Benefits offered by this job
Job summary
Johnson & Johnson Innovative Medicine is seeking a Director, World Model & Agentic Learning to lead the AI science team responsible for enterprise world models and agentic learning within the Generative AI organization. You will shape technical direction and deliver a durable, product‑oriented capability integrated with existing data and governance layers.
You will recruit and guide 4–8 AI scientists, partnering with domain experts to ensure scalable, auditable decisions across workflows, while
Qualifications
- 8+ years of post‑academic AI/ML industry experience.
- PhD or equivalent advanced degree preferred.
- Experience leading AI science teams and owning technical architecture.
- Ability to design systems that learn and improve from real‑world operation.
- Strong communication and collaboration across scientific and engineering teams.
Responsibilities
- Design how agents represent accumulated domain understanding and reason against it.
- Build mechanisms to expose confidence, boundaries, gaps, and contradictions.
- Ensure knowledge compounds across domains and workflows.
- Provide provenance‑grounded representations to reasoning agents with validated data.
- Lead a team of AI scientists and recruit top talent.
Skills
Education
Tools
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.
People Leader
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 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.
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 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.
- 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.
- 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.
- 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.
- 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’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.
- 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.
- 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.
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 & JJohnson 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.
This position will be located at one of our U.S. offices: Titusville, NJ; Spring House, PA; Cambridge, MA; or La Jolla, CA. Hybrid work arrangements apply.
Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact-us/careers , internal employees contact AskGS to be directed to your accommodation resource.
- Advanced Analytics
- Budget Management
- Compliance Management
- Critical Thinking
- Data Analysis
- Data Privacy Standards
- Data Quality
- Data Reporting
- Data Savvy
- Data Science
- Data Visualization
- Developing Others
- Digital Fluency
- Inclusive Leadership
- Leadership
- Program Management
- Strategic Thinking
- Succession Planning
- Advanced Analytics
- Budget Management
- Compliance Management
- Critical Thinking
- Data Analysis
- Data Privacy Standards
- Data Quality
- Data Reporting
- Data Savvy
- Data Science
- Data Visualization
- Developing Others
- Digital Fluency
- Inclusive Leadership
- Leadership
- Program Management
- Strategic Thinking
- Succession Planning
$164,000.00 - $282,900.00
Subject to the terms of their respective plans, employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
This position is eligible to participate in the Company’s long‑term incentive program.
- Vacation –120 hours per calendar year
- Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
- Holiday pay, including Floating Holidays –13 days per calendar year
- Work, Personal and Family Time - up to 40 hours per calendar year
- Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
- Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
- Caregiver Leave – 80 hours in a 52‑week rolling period10 days
- Volunteer Leave – 32 hours per calendar year
- Military Spouse Time-Off – 80 hours per calendar year
- For additional general information on Company benefits, please go to: - https://www.careers.jnj.com/employee-benefits
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