LILA has created the world's first Operating System for Science powered by Scientific Superintelligence™. Backed by General Catalyst.
About the role
Lila Sciences is building the infrastructure for autonomous science: AI systems that design experiments, run them in physical labs, and close the loop across life sciences, chemistry, and materials science. Our platform enables this to operate reliably at scale for external customers who require security, auditability, and operational rigor. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 8+ years of software development experience, including production ownership of a search, retrieval, RAG, or knowledge graph system that real users depended on
- Production experience building semantic search: embedding models, vector indexes (HNSW/IVF), hybrid lexical and vector retrieval, and reranking. Graph database experience is a strong plus
- Track record in high-growth environments where speed-to-value mattered as much as long-term architecture
- You can describe how you measured retrieval quality (recall, groundedness, citation accuracy) and how you caught regressions
- Hands-on coding in Python and modern backend frameworks (FastAPI, async Python)
- Modern cloud-native architectures (Kubernetes, Terraform, AWS)
More about this role
Lila Sciences is building the infrastructure for autonomous science: AI systems that design experiments, run them in physical labs, and close the loop across life sciences, chemistry, and materials science. Our platform enables this to operate reliably at scale for external customers who require security, auditability, and operational rigor.
We're hiring a Staff/Principal Engineer, Search & Retrieval (Enterprise AI) to own how scientists and agents ask questions of scientific knowledge at Lila: published literature and patents, customer-supplied documents, lab protocols, and the structured experimental record.
Your work will involve building RAG over unstructured text and a scientific knowledge graph that links literature to the structured experimental record.
Enterprise customers require one retrieval platform that any scientific workload can build on, with tenancy enforced in the index and the graph rather than the application layer. You will own that stack end to end, from ingestion to retrieval to cited answers a scientist can trace back to the evidence.
This is a deeply technical senior individual contributor role. We move fast, ship often, and look for engineers who do well...
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