Senior Software Engineer, Ontology & Reasoning Systems at Protegrity · New York, NY · Joined
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Senior Software Engineer, Ontology & Reasoning Systems
Protegrity · New York, NY · Just now · 0 applicants
NewHybridFull-timeSenior
Pay
$238k–$267kListed
Workplace
Hybrid
Job type
Full-time
Level
Senior
Team: Ontology & Reasoning Systems · Source: jobright · Posted by joined-scrapper
About the role
Protegrity is seeking a Senior Software Engineer to build an ontology and reasoning layer that unifies structured, semi-structured, and unstructured data for deterministic machine reasoning, using LLMs for ingestion with output validation. The role demands strong formal foundations and hands-on experience with logic-based reasoning, knowledge graphs, and production-grade backend systems.
What you’ll do
Build an ontology and reasoning layer unifying structured, semi-structured, and unstructured data
Implement explainable and traceable conclusions
Develop retrieval capabilities
Build production-grade systems
Use LLMs for ingestion with validation of their outputs
What you’ll need
5+ years building backend systems in a modern language such as Python, Go, Java, Scala, Rust, or equivalent practical experience
Strong formal and mathematical foundation including discrete mathematics, logic, and graph theory
Experience with declarative, logic-based, or rule-based reasoning systems such as Datalog, Answer Set Programming, Prolog, or SMT solvers
Practical experience building LLM-powered systems including structured extraction, RAG, or classification pipelines
Deep experience with knowledge graphs and graph data including RDF, SPARQL, property graphs, or graph databases such as Neo4j
Experience designing systems with provenance, derivation chains, or evidence trails
Original job description
Protegrity is seeking a Senior Software Engineer to build an ontology and reasoning layer that unifies structured, semi-structured, and unstructured data for deterministic machine reasoning. The role focuses on formal modeling, explainable and traceable conclusions, retrieval capabilities, and production-grade systems, using LLMs for ingestion with…