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Skills

RAG pipelines
Knowledge graphs
data modeling
entity resolution
Data governance
Stakeholder communication
Python
SQL
Vector Databases
Librarian
Information Retrieval
Graph Databases

Summary

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.

Why this role matters

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.

What you will actually do

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 turn around and explain the same decisions to a business leader or executive in terms that actually land. Expect to do both regularly.

What we are looking for

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) pairs 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).

Nice to have

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.

What this role is not

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, the reference architectures, and the production deployment patterns. 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.

Practical details

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. 

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: PTP9RPSxse3oBw3
Position Id: 9020319
Posted 14 days ago

Agentic AI Engineer - Healthcare AI

Arlington, Virginia

•

15d ago

Three hundred fifty million Americans rely on a healthcare system whose decision-making has become slow, costly, and adversarial - care delayed by prior authorization and paperwork, claims that misfire, clinical decisions made without the right information at the right moment, and patients who struggle to navigate or afford the care they need. Deloitte has a new AI-first effort, backed by $1B in committed investment, building the reasoning models and agentic systems to rebuild how that system de

Full-time

USD 134,500.00 - 265,100.00 per year

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