Lead Specialist, AI Scientist

Pearson VUE

. Design and build the knowledge graph and underlying knowledge model for Pearson’s K–12 assessment and learning domains .

Posted 11/4/2026full-timeRemote • Texas • United StatesSenior💰 $140,000 - $160,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Expertise in designing and building knowledge graphs and semantic models, with strong programming skills in Python and a deep understanding of AI systems. Proficient in data architecture, knowledge modeling, and integrating complex data sources while ensuring privacy and security.

Highest-signal resume keywords

Knowledge Graph DesignSemantic Schema DesignData ArchitecturePython ProgrammingPredictive AI Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Knowledge GraphsSemantic ModelingData IntegrationRDFOWLSPARQLData ModelingAPIsOntologiesProperty-Graph Schemas

Soft Skills

CommunicationCollaborationProblem-SolvingJudgmentAutonomy

Tools & Technologies

Graph TechnologiesAI SystemsData PipelinesKnowledge RepresentationHybrid AI Systems

Industry Keywords

K–12 StandardsPsychometricsAssessment DesignCompetency FrameworksEducational Measurement

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

Design and build the knowledge graph and underlying knowledge model for Pearson’s K–12 assessment and learning domains
Represent standards, competencies, skills, claims, evidence, task characteristics, misconceptions, content, and relationships as computationally usable structures
Establish identity, versioning, provenance, lineage, and lifecycle status for knowledge objects
Separate authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses
Evaluate and select graph and semantic technologies
Build pipelines, mappings, and synchronization processes connecting item banks, standards, metadata, psychometric data, and other sources
Develop schemas, APIs, data contracts, and services for applications, agents, and analytics
Integrate the knowledge layer into the assessment authoring system with software engineers
Design graph, semantic, vector, and hybrid retrieval and context architectures for AI systems
Create task-specific knowledge projections and maintain traceability between AI outputs and source knowledge
Collaborate with Applied AI Systems Engineers on generative, predictive, agentic, and analytic capabilities
Prototype rapidly, test assumptions empirically, evaluate emerging approaches, and make architectural decisions explicit
Design transparent, auditable, and traceable AI reasoning systems
Establish quality, coverage, and consistency measures for knowledge assets
Uphold privacy, security, access control, and appropriate use of student and content data
Collaborate with Technology, AI Science, software engineering, psychometrics, learning science, content development, and subject-matter experts
Communicate complex architectural concepts to technical and non-technical audiences

Requirements

What you’ll need

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, including ontologies, property-graph schemas, vocabularies, and taxonomies
Strong command of 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 experience building working systems and APIs
Deep understanding of predictive and generative AI, including retrieval-grounded and agentic systems using structured knowledge
Track record of building novel systems without established solutions, with sound judgment and high autonomy
Ability to translate expertise from other disciplines into computational representations and communicate tradeoffs clearly
Preferred: understanding of K–12 standards, competency frameworks, skills taxonomies, or credential frameworks
Preferred: knowledge graphs powering content generation, framework alignment, personalized guidance, or analytic insights
Preferred: formal ontology engineering, including OWL-based modeling, reasoning, or validation
Preferred: hybrid AI systems combining machine-learned or generative models with structured knowledge, rules, or constraints
Preferred: graph machine learning, graph embeddings, link prediction, or graph neural networks
Preferred: experience in evidence- or expert-judgment-validated domains
Preferred: evaluation datasets, human annotation and adjudication workflows, or benchmark development
Preferred: educational measurement, psychometrics, principled assessment design, or assessment content systems
Preferred: publications, patents, or open-source contributions in knowledge representation or graph technologies

Benefits

Comp & perks

Eligibility to participate in an annual incentive program
Benefits information available via the employer's benefits page