Senior Staff Applied AI Engineer - Context Retrieval

Databricks is the Data and AI company, serving more than 20,000 organizations worldwide, including 70% of the Fortune 500. They build a unified data and AI platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog.

Senior Staff Applied AI Engineer to own context retrieval for Databricks agents across SaaS providers. This is a zero-to-one role with two charters: build the retrieval stack (query understanding, content understanding, ranking, retrieval, evaluation) and build search subagents that reason about context retrieval.

• Build the full retrieval stack from scratch, including query understanding, content understanding and indexing, hybrid retrieval, ranking, and evaluation.
• Retrieve across heterogeneous data—structured (tables, columns, SQL queries, dashboards, code, notebooks, jobs) and unstructured (docs, wikis, tickets, chat, images, video, audio).
• Build connectors and retrieval adapters for enterprise SaaS systems.
• Optimize retrieval for both LLMs (grounded, token-efficient, hallucination-resistant) and humans (intuitive, explainable discovery).
• Crack query understanding for agents: query rewriting, decomposition, intent classification, entity resolution.
• Build content understanding pipelines that extract structure, entities, embeddings, summaries, and metadata.
• Design search subagents that plan multi-hop searches, issue follow-up queries, ground claims, and signal failure.
• Build evaluation flywheel: offline evals (nDCG, MRR, Recall@K, Precision@K), LLM-as-judge harnesses, human-in-the-loop labeling, online experimentation.
• Set technical direction, mentor senior engineers, and partner with Research, Product, and Platform leaders.

• 10+ years of software engineering experience, with significant time building production retrieval, search, or RAG systems at scale.
• Deep Information Retrieval expertise: lexical retrieval (BM25, Lucene/Elasticsearch/OpenSearch), dense retrieval (embeddings, ANN indexes—FAISS, ScaNN, HNSW), hybrid retrieval, learning-to-rank.
• Hands-on experience with modern LLM-era retrieval: RAG architectures, query rewriting, re-ranking with cross-encoders, long-context strategies, grounding techniques.
• Experience designing agentic systems on top of retrieval—search planners, multi-hop/iterative retrieval, self-reflection, tool-using agents.
• Strong grasp of relevance evaluation: nDCG, MRR, Precision@K, Recall@K; offline/online experimentation; LLM-as-judge frameworks.
• Experience working across structured and unstructured data.
• Track record of building 0→1 systems.
• Demonstrated technical leadership.

Local Pay Range: $228,600—$342,800 USD. Total compensation may include annual performance bonus, equity, and benefits.

Comprehensive benefits and perks are provided; specific details vary by region.