Lead Specialist, AI Scientist

Emploi pas sur LinkedIn

🕒 il y a 1 jour

🤠 Texas – Distant

🤠 Texas – Remote

💵 $140 000 - $160 000 / an

⏰ Temps Plein

🟠 Senior

🧠 Chercheur en IA

👻 Score fantôme 0%

Ce que ça veut dire : la probabilité que personne ne recrute vraiment pour cette offre.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Pearson VUE

Site WebLinkedInTous les Emplois

1001 - 5000 employés

Fondée en 1994

💼 Conseil

🏥 Santé

📦 Logistique

Consulting • Healthcare • Logistics

Pearson VUE est un leader mondial des tests informatisés, offrant une vaste gamme d'examens de certification pour divers secteurs. Ils soutiennent les candidats et les propriétaires de tests en proposant des ressources, des options de programmation et des aménagements spéciaux pour assurer un accès équitable aux tests. Leur mission est de responsabiliser les candidats et d'enrichir les communautés à travers la réalisation d'examens critiques qui valident les compétences et les connaissances professionnelles, contribuant à l'avancement des carrières et aux normes de l'industrie.

Description

• 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

🎯 Exigences

• 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

🏖️ Avantages

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

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