Principal & Data Architect

Location

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

Primary Work Address: 4000 Jones Bridge Road, Chevy Chase, MD, 20815 Current HHMI Employees,  to apply via your Workday account. 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 Everyday AI 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.

• 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.
• 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.
• Build and manage deduplication, linking, and canonicalization solutions to support entity resolution across systems.
• 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 to define contracts and data flows.
• Translate knowledge‑architecture trade‑offs for engineering teams and explain decisions to business leaders or executives in plain terms, doing both regularly.

• Real production RAG experience. Proven experience shipping retrieval‑augmented systems and running them in production, debugging failures, and rebuilding broken steps.
• Hybrid retrieval, chunking strategy, query understanding, and re‑ranking used as working tools.
• Knowledge management and data modeling. Strong instinct for content ownership, entity and relationship identification, and rationale for representation choices.
• Knowledge graph and entity resolution experience. At least one knowledge graph designed, built, and operated in production with clear justification for graph usage.
• Deduplication and linking problems solved, including handling multiple names across systems.
• Information extraction and production rigor. Pipelines built to turn unstructured text into structured knowledge with embedding versioning, retrieval evaluation, corpus drift, and re‑indexing managed as engineering concerns.
• Data engineering, security, and access control fluency. Experience with SQL, Databricks, dbt, and ETL patterns, and designing for data classification, access…