Search Engineer, AI Infrastructure (SF/Toronto)

Search Engineer role owning ranking quality and relevance for an LLM-driven search and retrieval product. The company builds the data layer for AI agents, turning URLs into clean structured data. The role focuses on improving search quality while navigating web-scale retrieval constraints like freshness, deduplication, latency, reliability, and cost. Design and build crawling, indexing, and retrieval systems. Push down latency and cost per query. Own services end to end. Work with Head of Search

3+ years experience building production search/retrieval systems with ranking and relevance as primary discipline. Must have a specific example of a ranking change and its metric impact. Hands-on depth in BM25, semantic/vector retrieval, hybrid retrieval, re-ranking, RAG evaluation. Experience measuring search quality via relevance, recall, conversion, CTR, NDCG, MRR. End-to-end ownership of search services. Strong backend skills in Go, Rust, Python. Balance relevance, latency, cost, correctness