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Knowledge Engineer Jobs in Virginia (NOW HIRING)

Senior Data Engineer - Knowledge Graphs

Reston, VA · On-site

$119K - $143K/yr

This may come from a traditional data engineering background with hands-on knowledge graph experience, or from a research-oriented knowledge graph / semantic systems background paired with proven ...

Senior Data Engineer - Knowledge Graphs

Reston, VA · On-site

$119K - $143K/yr

This may come from a traditional data engineering background with hands-on knowledge graph experience, or from a research-oriented knowledge graph / semantic systems background paired with proven ...

Senior Data Engineer - Knowledge Graphs

Herndon, VA · On-site

$117K - $141K/yr

This may come from a traditional data engineering background with hands-on knowledge graph experience, or from a research-oriented knowledge graph / semantic systems background paired with proven ...

Knowledge Management

Reston, VA

$43K - $50K/yr

Morfologica, Inc. is seeking several Knowledge Management Experts who are willing to support established teams of software engineers at various Federal Facilities throughout Northern Virginia.

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Knowledge Engineer information

See Virginia salary details

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$47

$72

How much do knowledge engineer jobs pay per hour?

As of Jun 23, 2026, the average hourly pay for knowledge engineer in Virginia is $47.20, according to ZipRecruiter salary data. Most workers in this role earn between $38.85 and $57.21 per hour, depending on experience, location, and employer.

What are some common challenges Knowledge Engineers face when collaborating with subject matter experts (SMEs)?

Knowledge Engineers often work closely with subject matter experts to extract and formalize complex domain knowledge into structured formats for systems like knowledge bases or AI applications. One common challenge is bridging the communication gap, as SMEs may use specialized jargon or have implicit knowledge that's difficult to articulate. Ensuring accuracy while translating this expertise into machine-readable forms requires patience, active listening, and iterative feedback. Building strong relationships and developing effective questioning techniques are essential for overcoming these challenges and delivering high-quality knowledge models.

What engineers make $500,000?

Senior engineers in fields such as software engineering, data engineering, and specialized roles like machine learning engineers can earn $500,000 or more annually, especially with experience, advanced skills, and stock options. High compensation is often associated with leadership positions, working at large tech companies, or in high-demand industries requiring expertise in cloud computing, AI, or cybersecurity.

What are Knowledge Engineers?

Knowledge Engineers are professionals who design, develop, and maintain systems that simulate human knowledge and reasoning. They work at the intersection of computer science, artificial intelligence, and domain expertise to gather and structure information so that machines can use it to solve complex problems. Their responsibilities often include creating knowledge bases, developing ontologies, and implementing rules or logic to enable decision-making in expert systems or AI applications. Knowledge Engineers play a crucial role in enabling organizations to leverage AI for problem solving and automation.

What does a knowledge engineer do?

A knowledge engineer designs, develops, and maintains systems that capture and organize knowledge for artificial intelligence and expert systems. They analyze domain data, create ontologies, and implement rules using tools like knowledge bases and reasoning engines to enable machines to simulate human decision-making. Strong skills in logic, data modeling, and programming are essential for this role.

What is the difference between Knowledge Engineer vs Data Scientist?

AspectKnowledge EngineerData Scientist
Required CredentialsBachelor's or Master's in Computer Science, AI, or related fields; knowledge of ontologies and knowledge basesBachelor's or Master's in Data Science, Statistics, or related fields; proficiency in programming, statistics, and data analysis
Work EnvironmentTypically in AI development teams, focusing on knowledge systems and expert systemsOften in analytics teams, working with large datasets and predictive modeling
Employer & Industry UsageUsed in AI, robotics, and enterprise knowledge managementCommon in tech, finance, healthcare, and marketing sectors

While both roles involve working with data and information, Knowledge Engineers focus on structuring and encoding knowledge for AI systems, whereas Data Scientists analyze data to extract insights and build predictive models. Their skills and tools differ, but both are essential in data-driven industries.

Is AI replacing ECE jobs?

Knowledge engineers develop and manage AI systems, and while AI automation can impact certain tasks, it generally complements ECE (Electrical and Computer Engineering) roles rather than replacing them entirely. ECE professionals with skills in machine learning, programming, and hardware design continue to be essential for developing and maintaining AI technologies.

What are the key skills and qualifications needed to thrive as a Knowledge Engineer, and why are they important?

To thrive as a Knowledge Engineer, you need a solid background in computer science, logic, and knowledge representation, often supported by a relevant degree. Familiarity with semantic web technologies, ontology development tools (like Protégé), and knowledge management systems is typically required. Analytical thinking, attention to detail, and strong communication skills help you effectively translate complex information into structured, usable formats. These capabilities ensure that knowledge systems are accurate, interoperable, and valuable for organizational decision-making.

How much does a knowledge engineer make?

The average salary for a knowledge engineer typically ranges from $80,000 to $130,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in AI and machine learning can earn higher salaries, often exceeding $150,000.

What Is a Knowledge Engineer?

A knowledge engineer works with data and computer systems with the goal of making the technology imitate human thought processes to solve problems that typically require expertise. Working in a sector of artificial intelligence within the information technology field, a knowledge engineer looks at everyday processes and determines what course of thought a human takes to make a decision or begin an activity. They then create computer systems reliant on this extensive data to guide a machine to simulate human cognition and problem-solving. The data validation process is a vital aspect of a knowledge engineer’s job. They must gather an accurate understanding of the tasks at hand and ensure that the data meets specific standards.

What job categories do people searching Knowledge Engineer jobs in Virginia look for? The top searched job categories for Knowledge Engineer jobs in Virginia are:
Senior AI Engineer

$106K - $146K/yr

Full-time

Medical, Retirement, PTO

Posted 27 days ago


Job description

TSC is seeking a Senior Ontology Engineer in King George/Dahlgren, Virginia. This position supports the Systems Engineering & Analysis Division (SEA).

*Ability to obtain and maintain a SECRET DoD Clearance.

TSC offers a professional working environment, a competitive salary, and an excellent benefits package. Come and join our team!

We are seeking a Senior Ontology Engineer to design, develop, and maintain ontological frameworks and knowledge graph solutions in support of defense mission engineering programs. The ideal candidate combines deep expertise in formal ontology and semantic web technologies with hands-on experience in data integration, graph database systems, large language models, and knowledge-driven data architecture development. This individual will translate complex, multi-domain mission requirements into scalable knowledge representations and work alongside software engineers, systems engineers, and domain experts to deliver end-to-end solutions. The ideal candidate has experience in technical writing and will work alongside the business development team to propose innovative solutions to address requests for proposals.

Responsibilities:

This position is designed to be flexible, with responsibilities evolving to meet program needs, emerging technology trends, and opportunities for individual professional growth.

This position requires a technically deep and mission-focused individual with expertise in knowledge engineering, ontology development, and artificial intelligence technologies applied to defense and mission engineering domains.

Design, develop, and maintain formal ontologies using OWL, RDF/RDFS, and SPARQL aligned with DoD mission engineering requirements and semantic interoperability standards.
Build and deploy knowledge graph solutions that integrate structured and unstructured data sources across heterogeneous, multi-domain defense systems.
Apply and extend upper-level ontology frameworks, ensuring alignment with DoD and IC data standards.
Design and implement graph-based AI and semantic solutions, including LLM-integrated pipelines, RAG architectures, and agentic workflows that leverage knowledge graph representations.
Collaborate with engineers, systems architects, and mission-domain SMEs to translate operational requirements into actionable ontological models and knowledge architectures.
Support semantic integration and data interoperability efforts across legacy and modern system architectures, including graph database deployments (e.g., Stardog, Neptune, Neo4j).
Lead the development and writing of technical approaches for proposals related to supporting the Navy adopt AI technologies

Required Qualifications:

  • Bachelor's degree or higher in Computer Science, Information Science, Knowledge Engineering, or a related discipline; equivalent experience considered

  • PhD with 2+ years of relevant experience, MA/MS with 5+ years of relevant experience, or BA/BS with 7+ years

    • Hands-on experience with knowledge graph technologies including RDF, SPARQL, SHACL, and OWL for DoD or enterprise use cases

    • Experience with schema design, ontology management, and knowledge graph curation

    • Experience designing and developing end-to-end knowledge graph and AI data pipelines, including integration with LLMs or similar models

    • Familiarity with graph database platforms such as Stardog, Blazegraph, Neo4j, or Amazon Neptune

  • Ability to obtain and maintain a SECRET DoD Clearance

Preferred Qualifications:

  • Experience with U.S. Navy Combat Systems

  • 2+ years of hands-on Python experience, including frameworks such as TensorFlow, PyTorch, rdflib, or owlready2, and ETL pipeline tools (e.g., Apache NiFi, Airflow)

  • Experience with full-stack web development, including REST API design and development (e.g., FastAPI, Flask, or Node.js), front-end frameworks (e.g., React or Angular), and containerization/deployment tooling (e.g., Docker, Kubernetes)

  • Practical experience with NLP, semantic search, prompt engineering, and LLMs for enterprise-scale knowledge graph applications including RAG architectures.

  • Experience with agentic AI systems and multi-modal model integration applied to knowledge engineering problems

  • Demonstrated team leadership experience, with the ability to guide junior engineers and collaborate across engineering, research, and program management teams

  • PhD in Computer Science, Knowledge Engineering, Mathematics, or a related field, or a publication record in semantic web and knowledge representation

U.S. Citizenship Required for this Position: Yes

Job Type:Regular

Security Clearance: Secret (ability to obtain required)

Schedule:Full time (40 hr/week)

Travel:0-10%

TSC Benefits:

TSC offers a stable work environment, a competitive salary, and a comprehensive benefit package; including ESOP participation, 401k Plan, Flexible Work Schedules, Tuition Reimbursement, Co-Sponsored Health Plan, Paid Leave and much more.

Applying to TSC:

Only those candidates invited for an interview will be contacted. Employment at TSC is contingent upon the successful completion of a comprehensive background check, security investigation, and a drug screening.


This contractor and subcontractor shall abide by the requirements of 41 CFR 60-1.4(a), 60-300.5(a) and 60-741.5(a). These regulations prohibit discrimination against qualified individuals based on their status as protected veterans or individuals with disabilities, and prohibit discrimination against all individuals based on their race, color, religion, sex, sexual orientation, gender identity, national origin, or for inquiring about, discussing, or disclosing information about compensation. Moreover, these regulations require that covered prime contractors and subcontractors take affirmative action to employ and advance in employment individuals without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.