Director, World Model – Agentic Learning

Johnson & Johnson

. Lead the AI science team building Johnson & Johnson’s enterprise world model and agentic-learning capability for the R&D agentic AI platform .

Posted 9/30/2026full-timeUnited StatesLead💰 $164,000 - $282,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in building and shipping AI/ML systems, particularly in knowledge representation and continual learning. Proven ability to lead technical teams, establish accountability, and design auditable AI systems in regulated environments.

Highest-signal resume keywords

AI/ML Systems DevelopmentLarge Language ModelsKnowledge RepresentationPeople LeadershipTechnical Architecture

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Agentic FrameworksRetrieval-Augmented GenerationContinual LearningMemory-Based LearningKnowledge GraphsOntologiesStructured MemoryAuditable AI SystemsTechnical DirectionSystem Design

Soft Skills

Excellent CommunicationTeam BuildingTalent DevelopmentScientific RigorOwnership

Tools & Technologies

AI OperationsGenerative AIEnterprise PlatformsIT Delivery Organizations

Certifications & Qualifications

PhD in Computer ScienceAI/MLApplied MathematicsComputational Science

Industry Keywords

Life SciencesDrug DiscoveryPharmaceutical R&DRegulated EnvironmentsHigh-Stakes Environments

About the role

Key responsibilities & impact

Lead the AI science team building Johnson & Johnson’s enterprise world model and agentic-learning capability for the R&D agentic AI platform
Devise the approach, set technical direction, and lead delivery of a reusable, expert-curated capability
Design how agents represent accumulated domain understanding and reason against it
Build mechanisms for confidence, boundaries, gaps, contradictions, provenance, and auditability
Ensure knowledge compounds across domains and workflows
Serve grounded, queryable knowledge to reasoning agents and curate proposed knowledge through validation, deduplication, and conflict resolution
Build on existing context, memory, and governed data layers without rebuilding data pipelines
Design mechanisms that turn operation into improvement, including active learning, memory-based/in-context learning, and outcome-driven refinement
Partner with scientists and domain experts to apply expertise consistently at scale
Define and prove accountability by demonstrating improved decisions over time
Make conclusions auditable and reconstructable and evaluate decisions against real-world outcomes
Partner with J&J Technology, Generative AI evaluation, and AI operations teams
Recruit, build, and lead a team of 4–8 AI scientists
Attract, develop, and retain talent in continual learning, knowledge representation, and agentic systems
Establish a culture of scientific rigor, ownership, and accountability

Requirements

What you’ll need

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 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
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 to align scientists, engineers, domain experts, and senior stakeholders around a technical strategy
Advanced degree in computer science, AI/ML, applied mathematics, computational science, or a related discipline is preferred; PhD preferred
Experience at the intersection of AI and domain experts in regulated or high-stakes environments
Background in life sciences, drug discovery, or pharmaceutical R&D, or ability to ramp quickly in a scientific domain
Experience with knowledge graphs, ontologies, structured memory, or other explicit knowledge representations
Track record building auditable, traceable AI systems
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 at scale
Experience defining interfaces between knowledge/memory substrates and reasoning or agent systems

Benefits

Comp & perks

Retirement plan (pension)
Savings plan (401(k))
Long-term incentive program
Vacation – 120 hours per calendar year
Sick time – 40 hours per calendar year; 48 hours for Colorado employees; 56 hours for Washington employees
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 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
Inclusive interview process and disability accommodations