Staff/Principal Engineer, Search & Retrieval

The role

This person will own the search and retrieval stack for scientific knowledge, including RAG over unstructured text and a scientific knowledge graph. They will build the infrastructure for how scientists and agents ask questions of literature, patents, and lab protocols.

What you will do

• Provide technical leadership across platform engineering, infrastructure, AI/ML, and scientific teams.
• Define the shared retrieval foundation and architecture for scientific workloads.
• Build a scientific knowledge graph from literature and the structured experimental record.
• Own graph retrieval and its fusion with vector and lexical search.
• Build the autonomous research loop through agentic search.
• Own the retrieval pipeline end to end and the evaluation harness.
• Own the pipeline that turns scientific data into semantically searchable evidence.

Required qualifications

• 8+ years of software development experience, including production ownership of a search, retrieval, RAG, or knowledge graph system.
• Production experience building semantic search: embedding models, vector indexes (HNSW/IVF), hybrid lexical and vector retrieval, and reranking.
• Track record in high-growth environments.
• Ability to describe how retrieval quality was measured and how regressions were caught.
• Hands-on coding in Python and modern backend frameworks (FastAPI, async Python).
• Modern cloud-native architectures (Kubernetes, Terraform, AWS).
• Strong cross-functional communication and the ability to influence architecture across teams.
• BS, MS, or Ph.D. in Computer Science or a related field of study, or equivalent experience.

Nice to have

• Shipped RAG or semantic search in a regulated or enterprise context (SOC 2, HIPAA, GxP, or FedRAMP).
• Permission-aware retrieval over access-controlled data.
• Document understanding at scale: OCR, layout-aware parsing, table and figure extraction.
• Agentic retrieval and GraphRAG patterns.
• Platforms serving scientific or laboratory data such as LIMS, ELN, bioinformatics, or materials informatics.
• Domain-specific corpora and scientific ontologies (UMLS, MeSH, ChEBI, GO, UniProt, Materials Project).
• Knowledge graph construction and graph retrieval: property graph or RDF stores.
• Graph database experience

Benefits

• Medical, dental, and vision coverage
• Employer-paid life and disability insurance
• Flexible time off with generous company wide holidays
• Paid parental leave
• Educational assistance program
• Commuter benefits, including bike share memberships for office based employees
• Company subsidized lunch program

About the company

Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. The company builds systems that can learn for themselves by combining advanced AI models with proprietary AI Science Factory™ instruments.