Knowledge Graph Engineer

Babel Street is the trusted technology partner for the worlds most advanced identity intelligence and risk operations. We deliver advanced AI and data analytics solutions providing unmatched analysis-ready data regardless of language proactive risk identification 360-degree insights high-speed automation and seamless integration into existing systems. Babel Street empowers government and commercial organizations to transform high-stakes identity and risk operations into a strategic advantage. The actionable insights we deliver safeguard lives and protect critical assets around the world. Babel Street is headquartered in Reston Virginia with regional offices in Boston MA and Cleveland OH and international offices in Australia Canada Israel Japan and the U.K. For more information visit .

Role Summary

Babel Street is building an AI-native agent-ready API-first intelligence platform where computer vision NLP and document enrichment continuously produce signals about people places organizations and events. The knowledge graph transforms those signals into a coherent queryable representation of the world.

We are hiring a Knowledge Graph Engineer to help design build and operate the entity resolution and graph layer that connects our multimodal pipelines. You will work closely with the Director of Computer Vision and collaborate with ML platform and data engineering teams to connect mentions in text objects in images locations in geotagged media and entities in structured sources into a unified graph.

This is a hands-on engineering role where you will contribute to production systems grow your expertise in graph modeling and entity resolution and help shape a platform that is still evolving. This will be a hybrid role based out of either our Reston VA or Somerville MA office.

Key Responsibilities

• Implement and evolve graph schemas and entity models for people locations organizations images and events
• Build and maintain entity resolution pipelines that reconcile entities across NLP outputs (GLiNER spaCy) computer vision outputs (object detection OCR face recognition geolocation) and structured data sources
• Develop ingestion pipelines that write resolved entities into graph systems such as ArangoDB and Spanner Graph or BigQuery Graph
• Contribute to cross-modal linkage between text images and geospatial data including confidence scoring and provenance tracking
• Collaborate with computer vision engineers to integrate visual entity outputs into the graph layer
• Help build graph-backed APIs and services used by downstream products and workflows
• Write tests contribute to design discussions and support improvements to data quality and pipeline reliability

Why This Role Matters

The knowledge graph is where Babel Streets signals become usable intelligence. A detected landmark a name extracted from text an organization mentioned online and a face identified in an image all become more valuable when they resolve to a shared representation of an entity.

In this role you will help:

• Connect multimodal signals into a unified graph
• Improve the accuracy and consistency of entity resolution
• Enable downstream search analytics and agent workflows

Required Qualifications

• 25 years of professional software engineering experience (or equivalent experience through internships and projects)
• Proficiency in Python and at least one typed language (Java Go C# or TypeScript)
• Familiarity with at least one graph database or graph processing framework (ArangoDB Neo4j TigerGraph JanusGraph NetworkX or similar)
• Solid understanding of SQL and core data modeling concepts (keys normalization joins indexing)
• Ability to work across teams incorporate feedback and make steady progress in ambiguous problem spaces

Preferred Qualifications

• Exposure to entity resolution record linkage or deduplication
• Familiarity with semantic modeling or schema design (RDF OWL property graphs JSON-LD)
• Experience with cloud platforms such as GCP (Spanner BigQuery Cloud Run)
• Interest in computer vision NLP geospatial data or multimodal systems
• Experience testing data pipelines implementing data quality checks or working with observability tools
• Exposure to search ranking or retrieval-based systems

What Success Looks Like (First Six Months)

• Month 12: Ramp up on the graph stack contribute to existing pipelines and ship initial changes
• Month 34: Take ownership of a component or pipeline with guidance including testing and documentation
• Month 56: Deliver improvements to entity resolution quality graph ingestion or cross-modal linkage and contribute to design discussions

Benefits at Babel Street (just to name a few...)

• Health Benefits: Babel Street covers 85-100% monthly premium costs for Medical Dental Vision Life & Disability insurances for you and your family!
• Retirement Plans:Babel Street offers both a Traditional and Roth 401(K) with a very competitive match.
• Unlimited Flexible Leave: We trust our employees to manage their own time and balance their personal and work lives.
• Holidays: Babel Street provides employees with 12 paid Federal Holidays
• Tuition Reimbursement: We are committed to investing in our employees. One way we do that is with our Tuition Reimbursement Program for continuing education.

Babel Street is anequalopportunity/affirmative actionemployer. All qualified applicants will receive consideration foremploymentwithout regard to sex gender identity sexual orientation race color religion national origin disability protected Veteran status age or any other characteristic protected by law. FurtherBabel Street will not discriminate against applicants for inquiring about discussing or disclosing their pay or in certain circumstances the pay of their coworkerPay Transparency Nondiscrimination.In addition Babel Streets policy is to provide reasonable accommodation to qualified employees who have protected disabilities to the extent required by applicable laws regulations and ordinances where a particular employee works. Upon request we will provide you with more information about such accommodations.