Knowledge Graph Engineer at Babel Street in Reston, VA

Babel Street is the trusted technology partner for the world’s 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 www.babelstreet.com. 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 Street’s 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 2–5 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 1–2: Ramp up on the graph stack, contribute to existing pipelines, and ship initial changes Month 3–4: Take ownership of a component or pipeline with guidance, including testing and documentation Month 5–6: 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 an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. Further, Babel Street will not discriminate against applicants for inquiring about, discussing or disclosing their pay or, in certain circumstances, the pay of their co‐worker, Pay Transparency Nondiscrimination.In addition, Babel Street's 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. Range for this position based on qualifications and experience $140,000—$170,000 USD