Job Overview
Johnson & Johnson Innovative Medicine is recruiting a Director, World Model & Agentic Learning to join our Data, Data Science & AI organization. This newly created leadership role reports directly to the Head of Generative AI and will drive the development of a reusable, expert-curated enterprise world model and agentic-learning capability for the R&D agentic AI platform.
Key Responsibilities
- 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 compounds and surfaces wherever else it is relevant.
- Serve the representation to reasoning agents as a queryable, grounded knowledge base with provenance and confidence, and curate feedback 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 mechanisms that turn operation into improvement, such as active learning from expert corrections, memory-based or 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.
- Define and prove the accountability bar: demonstrate that the system produces better decisions over time and make every conclusion auditable and reconstructable.
- Partner with the J&J Technology, Generative AI evaluation, and AI operations teams to consume per-decision outcome signals as the learning signal and validate 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.
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 real‑world outcomes.
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.
- 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 and does not rebuild pipelines or own 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 or 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.
Location
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.
Equal Opportunity Employer
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.
Accessibility
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 , internal employees contact AskGS to be directed to your accommodation resource.
Pay & Benefits Transparency
The anticipated base pay range for this position is: $164,000.00 – $282,900.00.
Additional Description for Pay Transparency: 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)).
Additional benefits:
- 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 period
- 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:
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