AI Context and Ontology Development Intern

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

📋 Description

• Develop and expand the firm-wide ontology, defining various entity types, properties, and relationships that illustrate how Marketbridge delivers value across different practice areas, enabling AI systems to reason over this structure instead of solely relying on keyword retrieval.

• Create and enhance skill definitions for the AI context layer, establishing a schema that aligns each capability with the appropriate practice domain and engagement type, while constructing the metadata framework that governs the flow of context between agents and tools.

• Collaborate directly with Practice AI Leads to ensure that the ontology accurately represents how work is structured and delivered across the firm, identifying discrepancies between documented methodologies and actual operations, and addressing them through iterative revisions.

• Partner with the global AI product lead to outline how institutional expertise, client context, and delivery frameworks should interconnect throughout the firm, contributing to a knowledge architecture that facilitates the movement of intelligence across practices and geographies, rather than remaining isolated within individual teams.

• Design an iterative ontology management approach based on validation insights, refining entity relationships, resolving classification conflicts, and enhancing retrieval performance within the AI context layer.

• Collaborate with the engineering team to investigate architectural decisions, establish governance models, and set ontology maintenance standards to ensure that the context layer remains precise, auditable, and extensible as the platform evolves and the firm expands.

⛳️ Requirements

• Currently enrolled in a graduate or advanced undergraduate program in artificial intelligence, knowledge engineering, computer science, information science, or a related field with a focus on ontology, semantic technologies, or knowledge representation.

• Coursework or project experience in at least two of the following areas: ontology languages and modeling (OWL, RDF, SKOS), knowledge graph construction, semantic search and retrieval, LLM context window management, RAG architecture, or AI agent design and orchestration.

• Proven ability in structural thinking: you recognize that the effectiveness of an AI system’s reasoning heavily depends on how well its knowledge is organized, and you can create thorough artifacts without waiting for complete information.

• Comfortable navigating ambiguity: capable of asking the right questions to mitigate it, prioritizing tasks independently, and delivering structured outputs on time in a fast-paced environment.

• Experience in enterprise B2B settings, professional services, or consulting is advantageous. What is most important is the ability to work effectively at the intersection of conceptual rigor and practical execution.

🏝️ Benefits

• Hands-on experience

• Learning opportunities