Senior Staff Applied AI Engineer - Context Retrieval

At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

The Mission

Databricks agents are only as good as the context they can retrieve. Whether an agent is answering a question about last quarter's revenue, debugging a failing job, generating SQL against a 10,000-table lakehouse, or summarizing a Wiki page, its quality is bounded by what it can find — and how well it understands what it finds.

We are hiring a Senior Staff Applied AI Engineer to own context retrieval for Databricks agents across SaaS providers. This is a zero-to-one role with two deeply connected charters:

1. Build the retrieval stack — query understanding, content understanding, ranking, retrieval, and evaluation — across the Enterprise SaaS data stored across multiple systems.
2. Build the search subagents that sit on top of that stack and reason about what context is needed, how to retrieve it, and whether the right thing actually came back — closing the loop between an agent's intent and the substrate that serves it.

What You Will Do

• Build the full retrieval stack from scratch (query understanding, content understanding and indexing, hybrid retrieval, ranking, and evaluation)
• Retrieve across heterogeneous data — structured and unstructured (tables, columns, SQL queries, dashboards, code, notebooks, jobs, docs, wikis, tickets, chat, images, video, audio)
• Build connectors and retrieval adapters for SaaS systems where enterprise knowledge lives
• Optimize for two consumers at once: LLMs (grounded, token-efficient, hallucination-resistant context) and humans (intuitive, explainable discovery)
• Build query rewriting, decomposition, intent classification, and entity resolution tuned for multi-turn agentic workflows
• Build pipelines that extract structure, entities, embeddings, summaries, and metadata at scale
• Build search subagents that plan multi-hop searches, issue follow-up queries when results are weak, ground claims against retrieved evidence, and hand back high-confidence context
• Stand up offline evals (nDCG, MRR, Recall@K, Precision@K), LLM-as-judge harnesses, human-in-the-loop labeling, and online experimentation
• Set technical direction and grow the team

What We're Looking For

• 10+ years of software engineering experience, with significant time spent building production retrieval, search, or RAG systems at scale
• Deep Information Retrieval expertise: lexical retrieval (BM25, Lucene, Elasticsearch, OpenSearch), dense retrieval (FAISS, ScaNN, HNSW), hybrid retrieval, and 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 and sufficiency checks, tool-using agents
• Strong grasp of relevance evaluation: nDCG, MRR, Precision@K, Recall@K; offline/online experimentation; LLM-as-judge frameworks; building human labeling pipelines
• Track record of building 0→1 retrieval systems

Location

This role is based in our Mountain View, CA or San Francisco, CA office. Hybrid in-office collaboration expected.

Pay Range: $228,600 — $342,800 USD