Lead AI Knowledge Architect for Learning Systems

Pearson; Trenton (NJ); On-site; USD 140,000 - 160,000; Full time

Job summary

Pearson is seeking a Lead Specialist, AI Data & Knowledge Architect to design and build the knowledge foundation for our next generation of intelligent K–12 assessment and learning systems. This founding role sits at the intersection of content, learning science, AI, and psychometrics to enable scalable, machine-readable knowledge.

You will craft extensible graphs and schemas, guide AI retrieval, and collaborate with engineers to embed the knowledge layer across our assessment authoring system,

Qualifications

• Advanced degree in Computer Science, AI, Information Science or equivalent experience.
• Experience designing and building knowledge graphs in production or substantial applied settings.
• Hands-on knowledge modeling and semantic schema design using ontologies, vocabularies, and taxonomies.
• Strong command of graph and semantic technologies (RDF, OWL, SHACL, SPARQL, property graphs) and their architectural tradeoffs.
• Strong data architecture skills including data modeling, integration pipelines, identity, versioning, provenance, and lineage.
• Strong programming ability in Python or comparable languages with a track record of building working systems and APIs.
• Deep understanding of predictive and generative AI, including how retrieval-grounded and agentic systems use structured knowledge.
• Track record of building novel systems where no established solution existed, with high autonomy.
• Ability to translate expertise from other disciplines into computational representations and to communicate tradeoffs clearly.

Responsibilities

• Design and build the knowledge graph and underlying knowledge model for our assessment and learning domains, selecting among ontologies, schemas, vocabularies, and taxonomies as the problem requires.
• Represent standards, competencies, skills, claims, evidence, task characteristics, misconceptions, content, and their relationships as computationally usable structures.
• Design for extensibility across content areas, programs, assessment contexts, and future AI capabilities, starting simple without closing off future growth.
• Establish approaches for stable identity, versioning, provenance, lineage of knowledge objects.
• Clearly separate authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses.
• Evaluate and select graph and semantic technologies based on extensibility, interoperability, reasoning needs, maintainability, and downstream AI use.
• Build the pipelines, mappings, and synchronization processes that connect item banks, standards, metadata, psychometric data, and other sources into a coherent knowledge layer.
• Develop the schemas, APIs, data contracts, and services through which applications, agents, and analytic workflows access the knowledge architecture.
• Partner with software engineers to integrate the knowledge layer into our assessment authoring system.
• Design retrieval and context architectures that let AI and machine-learning systems use structured knowledge effectively, including graph, semantic, vector, and hybrid retrieval.
• Create task-specific knowledge projections so each capability receives what it needs without complex relationships being flattened into unstructured prompts.
• Maintain traceability between AI outputs and the knowledge used to produce them.
• Work with Applied AI Systems Engineers to ensure generative, predictive, agentic, and analytic capabilities operate reliably over the shared knowledge layer.
• Exercise a high degree of autonomy in shaping architecture where no established blueprint exists.
• Prototype quickly, test assumptions empirically, and change course when evidence warrants.
• Evaluate emerging approaches including graph-based, knowledge-grounded, and neuro-symbolic methods, and adopt those that show measurable value.
• Make architectural decisions, assumptions, and tradeoffs explicit and open to challenge from colleagues.
• Design knowledge systems that make AI reasoning transparent, auditable, and traceable to its sources.
• Support AI that strengthens human expertise rather than replacing it, with clear points for expert review and judgment.
• Partner with AI engineers, psychometricians, and content experts to test whether knowledge and retrieval designs measurably improve accuracy and reliability.
• Establish quality, coverage, and consistency measures for knowledge assets.
• Uphold responsible data practices, including privacy, security, access control, and appropriate use of student and content data.
• Partner with Technology teams and software engineers responsible for the assessment authoring system.
• Co-design architecture with AI Science colleagues and Applied AI Systems Engineers, providing and receiving technical challenge.
• Partner with psychometricians and learning scientists to represent measurement and learning concepts faithfully.
• Work with content development teams and subject-matter experts to capture domain knowledge, terminology, relationships, and constraints.
• Communicate complex architectural concepts clearly to technical and non-technical audiences.

Skills

Graph technologies

Semantic technologies

Data architecture

Python programming

Knowledge modeling

AI/ML fundamentals

Education

Advanced degree in Computer Science / AI / Information Science or equivalent

Tools

RDF/OWL/SHACL/SPARQL

Property graphs

Job description

Pearson is seeking a Lead Specialist, AI Data & Knowledge Architect to design and build the knowledge foundation for our next generation of intelligent K–12 assessment and learning systems. This founding role sits at the intersection of content, learning science, AI, and psychometrics to enable scalable, machine-readable knowledge.

You will craft extensible graphs and schemas, guide AI retrieval, and collaborate with engineers to embed the knowledge layer across our assessment authoring system,