Director, World Model – Agentic Learning

Jobtailor; California (MO); On-site; USD 180,000 - 280,000; Full time

Job summary

Johnson & Johnson Innovative Medicine seeks a senior AI leader to direct the AI science team building the enterprise world model and agentic-learning capability for our R&D platform. You will set the technical direction and lead a team to deliver scalable AI solutions that align with scientific goals.

You will collaborate with scientists and domain experts to encode knowledge, manage uncertainty, and ensure accountable decision making. Build and mentor a 4–8 person AI science team.

Qualifications

• 8+ years of post-academic industry experience shipping AI/ML systems and owning architecture.
• Deep expertise with LLMs, retrieval-augmented generation, agentic frameworks, and knowledge representation.
• Proven track record designing systems with memory and continual learning as core challenges.
• Experience designing systems that learn from real-world operation and expert feedback.
• Strong people leadership including recruiting and leading teams in a matrixed organization.
• Ability to defend a technical architecture and hold a team accountable to it.
• Excellent communication to align scientists, engineers, domain experts and executives.

Responsibilities

• Lead the AI science team that builds our enterprise world model and agentic-learning capability.
• Define approach, set the technical direction, and lead the team delivering it.
• Design how agents represent accumulated domain understanding and reason against it.
• Build mechanisms for the system to represent confidence, boundaries, gaps, and contradictions.
• Partner with scientists and domain experts for scalable application of expertise.
• Define and prove the accountability bar for better decisions over time.
• Recruit, build, and lead a team of 4–8 AI scientists.

Skills

AI leadership

Technical architecture

Team recruiting

Communication

Job description

Overview

• At Johnson & Johnson Innovative Medicine, lead the AI science team that builds our enterprise world model and agentic-learning capability for the R&D agentic AI platform.
• Devise the approach, set the technical direction, and lead the team that delivers it.
• 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.
• Partner with scientists and domain experts so their expertise becomes something the system can apply consistently at scale.
• Define and prove the accountability bar: demonstrate that the system produces better decisions over time.
• Recruit, build, and lead a team of 4–8 AI scientists.

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.

Core Competencies

Demonstrates extensive expertise in building and shipping AI/ML systems, with a focus on large language models, agentic frameworks, and knowledge representation. Proven leadership in recruiting and managing technical teams while aligning diverse stakeholders around a cohesive technical strategy.