Contract Knowledge Graph Jobs in Washington (NOW HIRING)

Primary Work Address: 4000 Jones Bridge Road, Chevy Chase, MD,20815

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HHMI is focused on supporting and moving science forward in a variety of different ways ranging from conducting basic biomedical research, empowering educators, inspiring students, developing the next generation of scientists – even stretching into film and media production. Our Headquarters is in the greater Washington, DC metro area and is home to over 300 employees with expertise in investments, communications, digital production, biomedical sciences, and everything in between. The work housed here supports and augments the groundbreaking research conducted in HHMI labs across the nation. As HHMI scientists continue to push boundaries in laboratories and classrooms, you can be sure that your contributions while working here are making a difference.

The EverydayAI Accelerator exists to turn generative AI into daily reality across HHMI’s administrative and operational functions. This role owns HHMI’s knowledge management layer for AI: the discipline of turning institutional information (documents, records, policies, scientific content, operational data) into structured, retrievable, trustworthy knowledge that AI systems can actually use.

The work is technical and grounded. You will design and operate the retrieval-augmented generation pipelines that every Accelerator project depends on: the chunking, embedding, indexing, and retrieval patterns that turn HHMI’s content into something AI can find and reason over. For use cases where a graph representation is the right tool (complex entity relationships, lineage, multi-hop reasoning), the knowledge graph gets built and operated alongside it. This work happens in partnership with the Principal AI Architect, who owns the AI platform and engineering foundation, and the Technology and Systems Management (TSM) Data Integrations team, who owns the data pipelines from source systems. Whoever holds this role designs the knowledge architecture and is accountable for operating it.

HHMI’s scientific, financial, and operational knowledge lives scattered across documents, databases, and systems never built to talk to AI. Without someone accountable for turning that information into something structured and trustworthy, every AI initiative at HHMI either repeats the same expensive groundwork or surfaces answers no one can stand behind. This role solves that problem once so that every Accelerator project and future AI effort can build on a governed, reliable knowledge foundation instead of reinventing it.

• Own HHMI’s knowledge management architecture. Design how institutional content is captured, structured, classified, retrieved, and maintained over time. Make the calls on representation (chunked text, embeddings, structured records, knowledge graphs, or hybrid) for each kind of content and each kind of use case, and own the consequences.
• Build and operate the RAG pipelines. Design and run the retrieval-augmented generation systems that every AI product at HHMI consumes, including document processing, chunking, embedding, indexing, hybrid retrieval, re-ranking, query rewriting. New projects inherit proven patterns; they do not roll their own.
• Build knowledge graphs where the use case requires it. For problems where graph representation is the right tool (complex entity resolution, multi-hop reasoning, lineage and provenance, relationship‑heavy queries), design the data model, stand up the graph store, and operate it.
• Extract structure from unstructured content. Build the pipelines that turn HHMI’s documents (policies, applications, financial records, scientific content) into something AI can consume. Use the right mix of LLM‑based extraction, classical NLP, and rule‑based methods for each source, and be able to explain why.
• Solve entity resolution. The same person, fund, application, or concept appears across many systems with many representations. Build the deduplication, linking, and canonicalization that lets the institution rely on a single, defensible truth.
• Govern knowledge classification and lineage. Sit in the AI governance group as the technical voice on knowledge sensitivity, provenance, and retention.
• Partner with Data Integrations and the AI platform team. TSM Data Integrations owns the plumbing across HHMI’s source systems, and you define what AI needs from it while co‑building the contracts that connect the two layers. The Principal AI Architect owns the AI platform; the knowledge layer it reasons over comes from this seat.
• Communicate across the altitude range. Translate knowledge‑architecture trade‑offs for engineering teams, then turnaround and explain the same decisions to a business leader or executive in terms that actually land. Expect to do both regularly.

• Real production RAG experience. Proven experience shipping retrieval‑augmented systems and running in production, with failures debugged back through the pipeline and the broken step rebuilt. Hybrid retrieval, chunking strategy, query understanding, and re‑ranking used as working tools, not just concepts. This is the core of the role.
• Knowledge management and data modeling. A librarian’s instinct for content (what’s authoritative, what’s stale, who can see it), plus the ability to look at an unfamiliar domain and identify the right entities, relationships, and representation, defending why an attribute is a node, an edge, or not modeled at all.
• Knowledge graph and entity resolution experience. At least one knowledge graph designed, built, and operated in production, with a clear sense of when a graph beats a vector store or document chunk. Deduplication and linking problems solved where the same thing has seven names across four systems and none of them are wrong.
• Information extraction and production rigor. Extraction pipelines built to turn unstructured text into structured knowledge using a mix of LLMs, classical NLP, and rule‑based methods, treating embedding versioning, retrieval evaluation, corpus drift, and re‑indexing as first‑class engineering concerns. “The model gave the wrong answer” is a debuggable system, not a shrug.
• Data engineering, security, and range. Fluency in a data integration team’s tools (SQL, Databricks, dbt, ETL patterns) paired with designing around data classification, access controls, and PII handling from the first conversation rather than as a final review. Ability to lead engineers technically without a reporting line, and to explain the same decision to a non‑technical stakeholder in terms that help them choose.
• Technical range to operate at this level. Strong proficiency in Python and SQL. Production experience with vector databases (Postgres pgvector, Pinecone, Weaviate, Qdrant, or comparable) and embedding pipelines. Production experience with at least one graph database (Neo4j, AWS Neptune, JanusGraph, TigerGraph, Stardog, or comparable) and graph query languages (Cypher, SPARQL, or Gremlin). Working knowledge of modern NLP and information extraction. Fluency in the modern data stack (Databricks, dbt, or comparable).
• Education and experience. Bachelor’s degree or equivalent, plus at least eight years of hands‑on experience across data engineering, information retrieval, and applied machine learning, with at least three years focused on production knowledge management for AI systems (retrieval‑augmented generation, knowledge graphs, or both).

• Background in library or information science, formal ontology, or semantic web technologies (RDFS, OWL, SKOS).
• Experience with hybrid retrieval (graph + vector) and GraphRAG patterns.
• Familiarity with MCP, structured‑output patterns, and AI agent tool design.
• Experience with master data management, data catalogs, or lineage tooling at enterprise scale.
• Prior experience in research, academic, or mission‑driven institutional environments.

• A data engineering role. This role partners with TSM’s Data Integrations team on source‑system pipelines and the data warehouse, but doesn’t own that plumbing; it owns the knowledge layer that sits on top of it.
• An AI platform or AI infrastructure role. The Principal AI Architect owns the AI engineering foundation, the platform services, and the reference architectures. Your job is to ensure there is structured, retrievable, trustworthy knowledge for that platform to reason over.
• A pure research role. Staying current on the field (RAG, knowledge representation, GraphRAG, neuro‑symbolic methods) matters, but the work is building and operating production knowledge systems, not publishing about them.
• A role for someone whose RAG or graph experience is only academic or prototype‑scale. You need production scars: systems that have handled real users, real failure modes, and real corpus evolution over time.

This role is hybrid, with 3 days per week in‑person at HHMI’s offices in Chevy Chase, MD. It reports to the Director, AI Enablement.

We encourage qualified candidates who are eligible to work in the United States to apply. Please note, we are not able to sponsor a visa for this position at this time.

Our employees are compensated from a total rewards perspective in many ways for their contributions to our mission, including competitive pay, exceptional health benefits, retirement plans, time off, and a range of recognition and wellness programs. Visit our Benefits at HHMI site to learn more.

Compensation Range

$174,770.40 (minimum) – $218,463.00 (midpoint) – $284,001.90 (maximum)

Pay Type

Annual

HHMI’s salary structure is developed based on relevant job market data. HHMI considers a candidate's education, previous experiences, knowledge, skills and abilities, as well as internal consistency when making job offers. Typically, a new hire for this position in this location is compensated between the minimum and the midpoint of the salary range.

HHMI is an Equal Opportunity Employer

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