The Ontology Engineer is a foundational technical hire on the AI, ML and Data Science team specializing in Knowledge Representation. This role is responsible for designing and maintaining the formal ontological architecture that makes cross-organizational data alignment. This is not a taxonomy or metadata management role. It requires genuine formal depth in description logics, upper ontology theory, and the ability to reason about what an ontology commits to and what it leaves open.

Our platform sits between hospitals, distributors, GPOs, manufacturers, and regulators, enabling transactional execution, clinical data alignment, and analytics optimization across organizational boundaries. Each party maintains its own implicit ontology encoded in its schemas, workflows, and data. The Ontology Engineer will define the formal structures and processes that make alignment across them possible. These structures should be auditable, compositionally sound, and maintainable over a multi-year lifecycle as all parties' systems evolve.

This Engineer will work directly with teammates that are familiar with ontology formalisms and with domain experts who understand the operational realities of HCSC data. They will be expected to make and defend design decisions at the level of formal correctness, not just practical convenience, and to direct and evaluate LLM-assisted ontology discovery and enrichment pipelines with the rigor that formal alignment demands.

Essential Duties:

• Design and maintain the ontology, covering the canonical structural layer (organizations, items, contracts, transaction), source data ontologies (supporting the canonical) and the process layer (data curation, ontology matching, workflows).
• Establish the rules for when two records from different systems refer to the same thing, and when they don't — recognizing the answer can differ by use case.
• Establish mappings from trading partner source data to the canonical ontology, with documented provenance and validity conditions for each mapping.
• Author OWL 2 axioms for ontology components; validate logical consistency (e.g. reasoner); maintain ontology lifecycle (e.g. with ROBOT, SHACL).
• Align with governance team and practice.
• Grounded ontology discovery from data (and its uses) rather than schema declarations and metadata alone.
• Build, direct and evaluate LLM-assisted ontology extraction pipelines, define and enforce the human-in-the-loop validation standards for AI-generated ontological candidates.
• Collaborate with data quality engineers to establish formal feedback .
• Translate formal ontology design decisions into specification/implementation for graph and relational stores.
• Specify and implement SPARQL queries and graph schema requirements with sufficient precision to prevent implementation-level semantic loss.
• Collaborate with internal and external stakeholders including domain experts, data engineers, product managers, and integration partners to ensure ontological architecture supports transactional, clinical, and analytical requirements.
• Proactively monitor developments in formal ontology, knowledge representation, and LLM-assisted knowledge engineering to drive adoption of improved methods.

Competencies:

• Fluency in OWL 2 and description logics: able to read and write OWL axioms, understand what a reasoner computes and why, and diagnose inference failures without relying solely on tooling.
• Working knowledge of at least one upper ontology (e.g. BFO) and the ability to apply upper ontology commitments to a domain ontology correctly, including the continuant/occurrent distinction.
• Proficiency in knowledge graph technologies including RDF, OWL, and SPARQL; familiarity with property graph approaches (LPG, Cypher) and awareness of the semantic differences between RDF-based and property graph representations.
• Understanding of data integration: schema matching and mapping semantics, entity resolution, and the formal properties of multi-source alignment.
• Ability to interpret data profiling results (functional dependencies, inclusion dependencies) as ontological signals rather than purely as data quality metrics.
• Familiarity with LLM-assisted ontology extraction and enrichment pipelines, including the ability to evaluate LLM-generated ontological candidates against formal.
• Excellent communication skills for translating formal design to business stakeholders without losing precision and to engineers without losing formal correctness.
• Comfort working with partial/incomplete formal models, maintaining clear documentation of what remains unspecified and why.
• Requires minimal to no supervision on formal ontology design work.

Required Qualifications and Skills:

• Greater than 4 years of experience in knowledge engineering, ontology development, or a closely related formal methods discipline.
• Demonstrated experience building and maintaining domain ontologies in Protege or equivalent, with reasoner-validated consistency; not solely taxonomy or metadata management work.
• Experience with ROBOT or ODK for ontology lifecycle management (or similar): automated quality checks, versioning, release pipelines.
• Expertise in SPARQL and/or Cypher for querying ontology-aligned data stores; ability to write and evaluate queries that correctly reflect ontological intent.
• Demonstrated ability to interpret data profiling output and translate it into formal ontological claims; experience with empirical ontology discovery from data as well as top-down ontology design.
• Experience directing or evaluating LLM-assisted knowledge extraction pipelines with formal validation requirements.
• Proficiency in Python (or similar) for ontology tooling, pipeline scripting, and data analysis in support of knowledge engineering workflows.
• Experience working in multi-disciplinary teams where formal and domain knowledge must be integrated under operational constraints.

Preferred Qualifications and Skills:

• Bachelor's or advanced degree in Computer Science, Mathematics, Philosophy (logic/formal methods), Information Science, or a related hard science discipline.
• Familiarity with category theory as applied to data integration -- functors, natural transformations, limits and colimits as schema merge operations -- at literacy level or above; knowledge of CQL/AQL or categorical database theory is a plus.
• Experience with LinkML.
• Healthcare supply chain domain knowledge and ontological structures.
• Experience with BFO 2.0 and the OBO Foundry principles and standards.
• Familiarity with provenance models (why-provenance, how-provenance, where-provenance) and their implementation in ontology-aligned data systems.
• Experience with graph database platforms at production scale (Stardog, Amazon Neptune, or equivalent) and the operational considerations of ontology-driven graph deployments.
• Passion for staying at the cutting edge of knowledge representation, semantic alignment, and AI-assisted ontology engineering.
• Sense of humor.

Estimated Salary: $128,000 - $170,000

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