Director, World Model & Agentic Learning

Johnson & Johnson Innovative Medicine•Hopewell Township, NJ

1d•$164,000 - $282,900•Hybrid

About The Position

At Johnson & Johnson Innovative Medicine, we are seeking a Director, World Model & Agentic Learning to join our Data, Data Science & AI organization. This is a new leadership position within the Generative AI organization, reporting to the Head of Generative AI. The role involves leading an AI science team focused on building the enterprise world model and agentic-learning capability for the R&D agentic AI platform. This platform is designed to be a reusable, expert-curated foundation that domain teams can customize, and it improves with use. The Director will be responsible for devising the approach, setting technical direction, and leading the team that delivers this product-agnostic capability. The role has two primary mandates: 1. World Model: defining how agents represent and reason against accumulated domain understanding, rather than re-deriving everything from raw sources for each task. 2. Agentic Learning: establishing how this understanding grows with use, improving from operation rather than solely from retraining foundational models.

Requirements

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.

Nice To Haves

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.

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 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.

Benefits

Pension plan
401(k) savings plan
Long-term incentive program
Vacation – 120 hours per calendar year
Sick time - 40 hours per calendar year (varies by state)
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