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Role Overview

We are seeking a Lead Specialist, AI Data & Knowledge Architect , to design and build the knowledge foundation of Pearson's next generation of intelligent K-12 assessment and learning systems.

This role sits within the Measurement & Learning team and operates at the intersection of Content Development, Learning Science, Technology, AI Science, and Psychometrics, with responsibility for translating deep assessment and measurement expertise into extensible computational representations. Prior assessment experience is helpful but not required.

This work will build a deep, layered knowledge architecture of interconnected graphs beneath our assessment authoring system. It will ground the system's AI agents and intelligent workflows, and support both formative and summative assessment. Standards, competencies, claims, evidence, task designs, scoring, and content will be represented as connected, machine-readable knowledge that AI, measurement, and content workflows can reason over.

This is a founding, hands-on role for someone eager to innovate at the edge of current practice: building what does not yet exist, testing it rigorously, and adapting quickly. The architecture will support predictive, statistical, graph-based, deterministic, and generative AI methods alike.

Knowledge Architecture & Modeling

+ 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, and lifecycle status of knowledge objects

+ Clearly separate authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses

Graph & AI Data Architecture

+ 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

Knowledge-Grounded AI, Retrieval & Context

+ 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

Innovation & Technical Judgment

+ 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

Responsible AI, Quality & Evaluation

+ 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

Cross-Functional Collaboration

+ 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

Required Qualifications

+ Advanced degree in Computer Science, Artificial Intelligence, Information Science, Knowledge Engineering, or a related field, or equivalent experience

+ Demonstrated experience designing and building knowledge graphs in production or substantial applied settings

+ Hands-on experience with knowledge modeling and semantic schema design, using approaches such as ontologies, property-graph schemas, vocabularies, and taxonomies

+ Strong command of graph and semantic technologies (e.g., 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 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 sound judgment and high autonomy

+ Ability to translate expertise from other disciplines into computational representations and to communicate tradeoffs clearly.

Preferred Qualifications

+ Strong understanding of how educational knowledge is structured, through K-12 standards, competency frameworks, or skills-based architectures such as skills taxonomies and credential frameworks, ideally from modeling them as structured data

+ Experience with knowledge graphs that power applications such as content generation, framework alignment, personalized guidance, or analytic insights

+ Experience with formal ontology engineering, including OWL-based modeling, reasoning, or validation

+ Experience building hybrid AI systems that combine machine-learned or generative models with structured knowledge, rules, o...

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