Staff/Principal Engineer, Search & Retrieval
Core
Building the shared retrieval foundation for autonomous science, including RAG over unstructured text and a scientific knowledge graph linking literature to experimental records.
Role type
Staff/Principal Engineer, Search & Retrieval (Enterprise AI)
Builds
A unified retrieval platform for scientific workloads, fusing vector, lexical, and graph search into cited answers.
Domain
Life sciences, chemistry, materials science, Enterprise AI
Deliverable
production ML models
Required skills
Semantic search (embedding models, vector indexes, hybrid retrieval, reranking), Knowledge graph construction and retrieval, Agentic search and query planning, Ingestion and extraction pipelines, Python, Cloud-native architectures (Kubernetes, Terraform, AWS)
Preferred skills
Graph databases, Document understanding at scale (OCR, layout-aware parsing), Agentic retrieval and GraphRAG patterns, Scientific ontologies (UMLS, MeSH, etc.), Permission-aware retrieval
Technologies
Python, FastAPI, HNSW, IVF, Kubernetes, Terraform, AWS, Knowledge Graphs
Responsibilities
Define the shared retrieval architecture, Build the scientific knowledge graph and fusion with vector search, Own the retrieval pipeline and evaluation harness, Build the autonomous research loop, Own ingestion and extraction pipelines
Seniority
Staff/Principal, hands-on IC with broad architectural influence
