Lead Specialist, AI Scientist

This is a fully remote position, open to applicants in Texas.

📋 Description

• Design and construct the knowledge graph along with the foundational knowledge model for Pearson’s K–12 assessment and learning sectors.

• Represent standards, competencies, skills, claims, evidence, task characteristics, misconceptions, content, and relationships as computationally usable entities.

• Define identity, versioning, provenance, lineage, and lifecycle status for knowledge objects.

• Differentiate between authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses.

• Assess and select graph and semantic technologies.

• Create pipelines, mappings, and synchronization processes that link item banks, standards, metadata, psychometric data, and various other sources.

• Develop schemas, APIs, data contracts, and services tailored for applications, agents, and analytics.

• Integrate the knowledge layer into the assessment authoring system alongside software engineers.

• Design graph, semantic, vector, and hybrid retrieval and contextual architectures for AI systems.

• Generate task-specific knowledge projections while ensuring traceability between AI outputs and source knowledge.

• Collaborate with Applied AI Systems Engineers on generative, predictive, agentic, and analytical capabilities.

• Rapidly prototype, empirically test assumptions, evaluate emerging methodologies, and clarify architectural decisions.

• Design AI reasoning systems that are transparent, auditable, and traceable.

• Establish quality, coverage, and consistency metrics for knowledge assets.

• Ensure privacy, security, access control, and appropriate usage of student and content data.

• Work in collaboration with Technology, AI Science, software engineering, psychometrics, learning science, content development, and subject-matter experts.

• Effectively communicate complex architectural concepts to both technical and non-technical audiences.

⛳️ Requirements

• An advanced degree in Computer Science, Artificial Intelligence, Information Science, Knowledge Engineering, or a related field, or equivalent experience.

• Proven experience in designing and constructing knowledge graphs in production or significant applied environments.

• Practical experience with knowledge modeling and semantic schema design, encompassing ontologies, property-graph schemas, vocabularies, and taxonomies.

• Strong proficiency in RDF, OWL, SHACL, SPARQL, property graphs, and their architectural trade-offs.

• Robust skills in data architecture, including data modeling, integration pipelines, identity, versioning, provenance, and lineage.

• Strong programming skills in Python or similar languages, with a proven track record of building operational systems and APIs.

• In-depth knowledge of predictive and generative AI, particularly retrieval-grounded and agentic systems utilizing structured knowledge.

• A history of creating innovative systems in the absence of established solutions, demonstrating sound judgment and a high degree of autonomy.

• Ability to translate expertise from diverse disciplines into computational representations and clearly articulate trade-offs.

• Preferred: Familiarity with K–12 standards, competency frameworks, skills taxonomies, or credential frameworks.

• Preferred: Experience with knowledge graphs that enhance content generation, framework alignment, personalized guidance, or analytical insights.

• Preferred: Background in formal ontology engineering, including OWL-based modeling, reasoning, or validation.

• Preferred: Experience with hybrid AI systems that integrate machine-learned or generative models with structured knowledge, rules, or constraints.

• Preferred: Knowledge of graph machine learning, graph embeddings, link prediction, or graph neural networks.

• Preferred: Experience in domains validated by evidence or expert judgment.

• Preferred: Familiarity with evaluation datasets, human annotation and adjudication workflows, or benchmark development.

• Preferred: Background in educational measurement, psychometrics, principled assessment design, or assessment content systems.

• Preferred: Publications, patents, or open-source contributions in knowledge representation or graph technologies.

🏝️ Benefits

• Eligibility to participate in an annual incentive program.

• Benefits information is accessible via the employer's benefits page.