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Knowledge Engineer Jobs in Virginia (NOW HIRING)

Morfologica

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

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$21

$47

$72

How much do knowledge engineer jobs pay per hour?

As of Jul 14, 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.

Which 5 jobs will survive AI?

Knowledge engineers design and refine AI systems by creating data models and algorithms, making their roles less susceptible to automation. Jobs that require complex problem-solving, emotional intelligence, creativity, and specialized expertise—such as healthcare professionals, educators, software developers, researchers, and skilled tradespeople—are more likely to endure AI advancements. Continuous learning and adapting to new tools are essential for these roles to remain relevant.

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 information, create knowledge bases, and use tools like ontologies and rule-based systems 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.

What engineers make $200,000 a year?

Senior software engineers, data engineers, and certain specialized roles such as machine learning engineers often earn $200,000 or more annually, especially with experience, advanced skills, and in high-demand industries. Compensation can also include bonuses, stock options, and other benefits, depending on the company and location.

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.

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.

More about Knowledge Engineer jobs
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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:
What are popular job titles related to Knowledge Engineer jobs in VA? For Knowledge Engineer jobs in VA, the most frequently searched job titles are:
Knowledge Engineer jobs near you
Infographic showing various Knowledge Engineer job openings in Virginia as of July 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $98,177 per year, or $47.2 per hour.
Knowledge Graph Software Engineer - Clearance Required

Knowledge Graph Software Engineer - Clearance Required

BTI360

Herndon, VA • On-site

Full-time

Posted 14 days ago

Job description

Job Summary:
BTI360 is a company focused on developing software engineers and has been recognized as a top workplace. They are seeking a Knowledge Graph Software Engineer to transform raw data into meaningful insights using AI technologies and to lead the development of knowledge graphs and databases.
Responsibilities:
• Lead end-to-end knowledge graph and knowledge base development efforts from problem definition to production, designing pipelines that extract, normalize, link, and organize information into scalable graph-based systems.
• Design and evaluate extraction and resolution workflows using sound methodologies and fit-for-purpose metrics to assess entity extraction, linking, relationship quality, and overall knowledge base completeness and accuracy.
• Translate business requirements into quantitative problems and communicate technical findings to both technical and non-technical stakeholders through reports, presentations, and direct customer engagement.
• Drive technical decision-making for schema design, ontology alignment, extraction approaches, and graph architecture based on mission needs, data quality, and long-term maintainability.
• Stay current with advances in knowledge representation and information extraction and introduce practical techniques, tools, and frameworks that improve graph construction, curation, and analytic value.
• Apply analytical and statistical methods to validate extracted insights, measure data quality, and support confident decision-making from structured and unstructured sources.
• Develop reports and whitepapers that evaluate solution alternatives based on impact, cost, technical feasibility, and alignment with strategic goals.
• Collaborate across teams to align on strategy, provide data science expertise, and contribute to proposals and strategic initiatives.
• Mentor junior data scientists by providing technical guidance, defining project direction, and sharing best practices in graph-oriented data modeling, extraction workflows, and knowledge base stewardship.
Qualifications:
Required:
• Active Security Clearance (Secret or higher) or the ability to obtain one
• Hands-on experience with graph databases such as Amazon Neptune, Neo4j, or related graph technologies.
• Experience designing and implementing scalable, maintainable, and OOP based software in a containerized cloud environment (AWS preferred) leveraging foundational services for computing, identity management, and networking.
• Experience developing backend services using Java and the Spring/Spring Boot framework (or similar relevant Java framework)
• Familiarity with API standards such as REST and HTTP, message-driven architectures, persistent storage layers, and distributed systems
• Effective written and verbal communication skills necessary to perform job duties and collaborate with team members
• Candidates must maintain a primary residence within a two hour drive of Herndon, VA to support onsite collaboration as needed.
Preferred:
• Experience with source control (e.g. Git) and CI/CD pipeline tools such as AWS CodeBuild (preferred), Jenkins, GitLab CI, or GitHub Actions
• Experience with testing frameworks such as Junit (preferred), Mockito, or Spring Runner
• Familiarity with monitoring and observability stacks such as Prometheus/Grafana (preferred), CloudWatch, or ELK/EFK
• Experience with search technologies such as OpenSearch (preferred), Elasticsearch, or Solr
• Experience working with streaming or event-driven architectures such as SNS/SQS (preferred), Kafka, Kinesis, AWS Step Functions, or Event Bridge
• Familiarity with monitoring and observability stacks including OpenTelemetry, Splunk (preferred), Prometheus/Grafana, or CloudWatch
Company:
BTI360 develops and delivers big data software solutions to minimize the time spent on research and utilize time providing insights. Founded in 2004, the company is headquartered in Ashburn, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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