Senior Ontology and Knowledge Engineer
Posted Aug 19, 2026 · We last checked this listing on Oct 5, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a knowledge graph project you contributed to that reached production.
What they are checking: Whether they have taken semantic work beyond research into something products and users rely on.
Describe how you've worked with subject matter experts who didn't know anything about ontologies.
What they are checking: Whether they can draw out domain knowledge from experts without burying them in modeling jargon.
What would you do
Two business units define 'supplier' differently. How do you settle that in the ontology?
What they are checking: Whether they can facilitate agreement on meaning and model real differences without forcing a false single definition.
How do you explain the value of an ontology to a product manager who just wants a database table?
What they are checking: Whether they can make semantic work understandable and show its payoff in business terms.
About the work
Walk me through how you'd build an ontology starting from a client's competency questions.
What they are checking: Whether they follow a disciplined method that keeps the model tied to real questions it must answer.
How do you decide whether something should be a class, a property or an individual in OWL?
What they are checking: Whether they have the modeling judgment that keeps ontologies clean and usable over time.
How would you use SHACL or similar tools to validate data against your semantic model?
What they are checking: Whether they can make semantic models enforceable rather than just documentation.
How do you align an internal ontology with healthcare standards like SNOMED CT, RxNorm or ICD-10?
What they are checking: Whether they reuse established vocabularies and map to them instead of reinventing healthcare concepts.
How do you version and govern an ontology when many teams depend on it?
What they are checking: Whether they manage change carefully so updates don't break downstream data products and AI solutions.
How can a well-built ontology improve the results of an AI or LLM application?
What they are checking: Whether they understand how semantic grounding reduces ambiguity and errors in AI outputs.
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