Systems Engineer Jobs

About The Position

Requirements

• Bachelor’s degree in Systems Engineering, Computer Science, Information Science, or a related field, plus 8 years of experience; or Master’s degree plus 6 years of experience.
• Experience designing enterprise-level data architectures, knowledge models, or information taxonomies that span multiple business domains.
• Strong understanding of ontology and semantic modeling concepts.
• Systems engineering mindset — you think about interfaces, dependencies, integration points, and emergent behavior across interconnected systems.
• Experience working across organizational boundaries — you have built consensus on shared standards across teams that had their own ways of doing things.
• Strong communication skills — you can explain data relationships to business stakeholders and architectural constraints to engineers.
• U.S. citizenship required.
• Department of Defense Secret security clearance is required at time of hire.

Nice To Haves

• Experience with knowledge graphs, semantic web technologies, or enterprise taxonomy management.
• Experience with enterprise data platforms (Palantir Foundry, Snowflake, or similar) and their ontology or semantic layer capabilities.
• Background in manufacturing, defense, or complex enterprise environments with multiple interacting business systems.
• Experience defining metadata standards, business glossaries, or data governance frameworks at an enterprise level.
• Familiarity with how AI/LLM systems consume structured knowledge — RAG architectures, knowledge-grounded reasoning, semantic search.

Responsibilities

• Define and maintain the overarching structure that connects domain-specific ontologies across pods.
• Manage the relationships between business vocabularies, taxonomies, and data models at the enterprise level.
• Ensure that business concepts defined in one pod are compatible with concepts in other pods.
• Resolve naming conflicts, semantic overlaps, and definitional inconsistencies.
• Establish enterprise-wide standards for metadata, tagging, classification, and search structures.
• Build the knowledge infrastructure that makes enterprise data findable, reusable, and machine-readable.
• Own the enterprise business glossary — the authoritative source for what terms mean across the organization.
• Work with data owners and business stewards to maintain accuracy of the business glossary.
• Design the structures that store and expose enterprise knowledge — knowledge graphs, semantic catalogs, taxonomy services.
• Ensure AI agents can discover and traverse enterprise knowledge programmatically.

Benefits

• Highly competitive benefits
• Flexible work environment where contributions are recognized and rewarded