• Lead by example to architect and code the next-generation scientific search engine, building a system that can reason over billions of scientific data points—from chemical structures (SMILES) to unstructured lab documents and instrument data. • Engineer sophisticated hybrid search pipelines that blend sparse (keyword), structured (metadata), and dense (vector) retrieval. You will go beyond out-of-the-box OpenSearch to design custom ranking logic, reciprocal rank fusion, and relevance tuning that surfaces the exact "needle in the haystack" for drug discovery. • Own and operate the Search Platform infrastructure, ensuring high availability, scalability, performance, and observability across indexing, embedding generation, and query execution. • Develop and maintain backend services and APIs in Python and TypeScript that power search capabilities for scientists, data engineers, and AI applications. • Collaborate with Applied AI Scientists to integrate embeddings, transformer models, and chemical fingerprints into production search workflows. • Architect and implement scientific entity resolution and knowledge graph pipelines to transform raw text into interconnected knowledge. You will design systems that extract and link chemical and biological entities (NER/NED) from unstructured documents, enabling the search engine to "understand" relationships between compounds, targets, and assays. • Continuously improve search quality through evaluation metrics such as precision@K, recall@K, MRR, and relevance testing with real scientific use cases. • Ensure security, compliance, and tenant isolation as part of operating search services in enterprise bio-pharma environments. • Contribute to architectural decisions, technical strategy, and platform-wide improvements to accelerate scientific insight generation.