Senior Staff Applied AI Engineer - Context Retrieval

Databricks Inc.; Mountain View (CA); On-site; USD 260,000 - 380,000; Full time

Job summary

Databricks is hiring a Senior Staff Applied AI Engineer to own context retrieval for Databricks agents across SaaS providers. This zero-to-one role focuses on building the end-to-end retrieval stack, from query understanding to evaluation, spanning structured tables, dashboards, code, docs, and more.

You will design connectors, rank content by freshness and permissions, and optimize for both LLM-driven grounding and human discovery.

Qualifications

• 10+ years of software engineering experience in retrieval or search systems.
• Significant experience building production retrieval, search, or RAG systems at scale.
• Deep Information Retrieval (IR) expertise: lexical, dense, and hybrid retrieval.
• Hands-on with modern LLM-era retrieval and RAG architectures.
• Experience designing agentic systems on top of retrieval.

Responsibilities

• Build the full retrieval stack from scratch for enterprise context retrieval.
• Retrieve across structured and unstructured data and index/rank across modalities.
• Develop connectors and adapters for SaaS data sources with freshness and permissions signals.
• Optimize retrieval for both LLMs and human users.
• Advance query understanding, decomposition, and entity resolution for agents.
• Develop content understanding pipelines and embeddings, summaries, metadata.
• Design and implement search subagents enabling multi-hop searches and grounded evidence.
• Build an evaluation flywheel with offline/online metrics and human-in-the-loop labeling.
• Define technical direction and mentor senior engineers.

Skills

Software engineering

IR expertise

BM25

Dense retrieval

Hybrid retrieval

LLM retrieval

Learning-to-rank

Multi-hop search

Agentic systems

Education

Bachelor's in Computer Science

PhD in related field

Tools

FAISS

ScaNN

Elasticsearch

OpenSearch

Lucene

Job description

P-1549 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.

Role

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:

• Build the retrieval stack — query understanding, content understanding, ranking, retrieval, and evaluation — across the Enterprise SaaS data stored across multiple systems.
• 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. Own the end-to-end system: query understanding, content understanding and indexing, hybrid retrieval, ranking, and evaluation. Make the architectural calls that will define how Databricks agents access context for years to come.
• Retrieve across heterogeneous data — structured and unstructured. Index and rank across structured assets (tables, columns, SQL queries, dashboards, code, notebooks, jobs) and unstructured content (docs, wikis, tickets, chat, images, video, audio). Each modality has its own signals — design retrieval that exploits them rather than flattens them.
• Connect to the SaaS surface area customers actually use. Build connectors and retrieval adapters for the systems where enterprise knowledge lives. Treat each retrieval source with its own freshness, permissions, and ranking signals.
• Optimize for two consumers at once. Retrieval must serve both LLMs (grounded, token-efficient, hallucination-resistant context) and humans (intuitive, explainable discovery). These are different objectives and require different signals — own both.
• Crack query understanding for agents. Agent queries don't look like web queries. Build query rewriting, decomposition, intent classification, and entity resolution tuned for multi‑turn agentic workflows.
• Crack content understanding at scale. Build the pipelines that extract structure, entities, embeddings, summaries, and metadata from every supported asset type — and keep them fresh as customer data evolves.
• Build search subagents that reason about retrieval. Design the agentic layer that decides what context is needed, which sources to query, how to decompose and route the search, and — critically — whether the retrieved content is actually sufficient to answer the question. These subagents will plan multi‑hop searches, issue follow‑up queries when results are weak, ground claims against retrieved evidence, and hand back high‑confidence context (or signal failure) to upstream agents. This is where IR meets agentic reasoning.
• Build the evaluation flywheel for both retrieval and subagents. Stand up offline evals (nDCG, MRR, Recall@K, Precision@K), LLM‑as‑judge harnesses, human‑in‑the‑loop labeling, and online experimentation. Extend evaluation beyond ranking metrics to measure subagent decision quality — did it ask the right follow‑up?, did it correctly recognize when retrieval failed?, did it ground its answer in the right evidence?. Quality you can’t measure is quality you can’t ship.
• Set technical direction and grow the team. Set the multi‑year roadmap, mentor senior engineers, partner with Research, Product, and Platform leaders, and raise the technical bar across the org.

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 (IR) expertise: lexical retrieval (BM25, Lucene/Elasticsearch/OpenSearch), dense retrieval (embeddings, ANN indexes — 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, and grounding techniques that reduce hallucination.
• Experience designing agentic systems on top of retrieval — search planners, multi‑hop / iterative retrieval, self‑