Senior Staff Applied AI Engineer - Context Retrieval - Databricks Skip to main content Login Why Databricks Discover For App Developers For Executives For Startups Lakehouse Architecture Databricks AI Research Customers Customer Stories Partners Partner Overview Explore the Databricks partner ecosystem Partner Spotlight Featured partner announcements Partner Program Explore benefits, tiers and how to become a partner Cloud Providers Databricks on AWS, Azure and GCP Find a Partner Discover Databricks partners for your needs Partner Solutions Find custom industry and migration solutions Product Databricks Platform Platform Overview A unified platform for data, analytics and AI Data Management Data reliability, security and performance Sharing Open, secure, zero-copy sharing for all data Data Warehousing Serverless data warehouse for SQL analytics Governance Unified governance for all data, analytics and AI assets Data Engineering ETL and orchestration for batch and streaming data Artificial Intelligence Build and deploy ML and GenAI applications Business Productivity Unified search, chat, dashboards and apps Business Intelligence Intelligent analytics for real-world data Application Development Quickly build secure data and AI apps Database Postgres for data apps and AI agents Security Open agentic SIEM built for the AI era Integrations and Data Marketplace Open marketplace for data, analytics and AI IDE Integrations Build on the Lakehouse in your favorite IDE Partner Connect Discover and integrate with the Databricks ecosystem Pricing Databricks Pricing Explore product pricing, DBUs and more Cost Calculator Estimate your compute costs on any cloud Open Source Open Source Technologies Learn more about the innovations behind the platform Solutions Databricks for Industries Communications Media and Entertainment Financial Services Public Sector Healthcare & Life Sciences Retail Manufacturing See All Industries Cross Industry Solutions AI Agents AI Governance Cybersecurity Marketing Migration & Deployment Data Migration Professional Services Solution Accelerators Explore Accelerators Move faster toward outcomes that matter Resources Learning Training Discover curriculum tailored to your needs Databricks Academy Sign in to the Databricks learning platform Certification Gain recognition and differentiation Free Edition Learn professional Data and AI tools for free University Alliance Want to teach Databricks? See how. Events Data + AI Summit Data + AI World Tour AI Days Event Calendar Blog and Podcasts Databricks Blog Explore news, product announcements, and more AI Blog Explore our AI research and engineering work Data Brew Podcast Let’s talk data! Champions of Data + AI Podcast Insights from data leaders powering innovation Get Help Customer Support Documentation Community Dive Deep Resource Center Demo Center Architecture Center About Company Who We Are Our Team Databricks Ventures Contact Us Careers Working at Databricks Open Jobs Press Awards and Recognition Newsroom Security and Trust Security and Trust DATA + AI SUMMIT JUNE 15–18 | SAN FRANCISCO Join us at the world’s largest data, apps and AI event. Register Ready to get started? Get a Demo DATA + AI SUMMIT JUNE 15–18 | SAN FRANCISCO Join us at the world’s largest data, apps and AI event. Register Login Try Databricks Overview Culture Benefits Diversity Engineering Research Students & new grads Back to search results Senior Staff Applied AI Engineer - Context Retrieval Mountain View, California; San Francisco, California Apply now 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. 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. If you have deep Information Retrieval wisdom, have shipped retrieval systems for RAG and agentic workloads, and want to build the substrate — and the agents on top of it — that make every Databricks agent measurably smarter, this role is for you. 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-reflection and sufficiency checks, tool-using agents that decide what to fetch and verify what came back. Strong grasp of relevance evaluation : nDCG, MRR, Precision@K, Recall@K; offline/online experimentation; LLM-as-judge frameworks; building human labeling pipelines. Experience working across structured and unstructured data — you've indexed and ranked over tables, code, and documents in the same system, and have opinions about how to do it well. Track record of building 0→1 : you've stood up a retrieval system from an empty repo, made the foundational architectural decisions, and grown it into something that customers depend on. Demonstrated ability to operate as a technical leader : setting direction across teams, mentoring senior engineers, and influencing roadmap with research, product, and platform partners. Nice to Have Experience building retrieval over enterprise SaaS sources (permissions, freshness, multi-tenancy, ACL-aware indexing). Background in agentic systems, tool use, or multi-turn retrieval for LLM agents. Contributions to open-source IR/search projects, or publications at SIGIR, KDD, WWW, EMNLP, or similar venues. Experience training or fine-tuning embedding models, rerankers, or query understanding models. Why This Role Foundational impact. Retrieval is the single biggest lever on agent quality. The stack you build will sit underneath every Databricks agent and every customer-built agent on our platform. Greenfield with scale. You get the rare combination of starting from a clean sheet and having immediate access to massive enterprise scale, real customer data, and a world-class research org. The right team. You'll work alongside engineers and researchers behind Lakehouse, Apache Spark™, Delta Lake, MLflow, MosaicML, and DBRX. Location This role is based in our Mountain View, CA or San Francisco, CA office. Hybrid in-office collaboration expected. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $228,600 — $342,800 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter , LinkedIn and Facebook . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone. Why Databricks Discover For App Developers For Executives For Startups Lakehouse Architecture Databricks AI Research Customers Customer Stories Partners Partner Overview Partner Program Find a Partner Partner Spotlight Cloud Providers Partner Solutions Why Databricks Discover For App Developers For Executives For Startups Lakehouse Architecture Databricks AI Research Customers Customer Stories Partners Partner Overview Partner Program Find a Partner Partner Spotlight Cloud Providers Partner Solutions Product Databricks Platform Platform Overview Sharing Governance Artificial Intelligence Business Intelligence Database Data Management Data Warehousing Data Engineering Business Productivity Application Development Security Pricing Pricing Overview Pricing Calculator Open Source Integrations and Data Marketplace IDE Integrations Partner Connect Product Databricks Platform Platform Overview Sharing Governance Artificial Intelligence Business Intelligence Database Data Management Data Warehousing Data Engineering Business Productivity Application Development Security Pricing Pricing Overview Pricing Calculator Open Source Integrations and Data Marketplace IDE Integrations Partner Connect Solutions Databricks For Industries Communications Financial Services Healthcare and Life Sciences Manufacturing Media and Entertainment Public Sector Retail View All Cross Industry Solutions AI Agents AI Governance Cybersecurity Marketing Data Migration Professional Services Solution Accelerators Solutions Databricks For Industries Communications Financial Services Healthcare and Life Sciences Manufacturing Media and Entertainment Public Sector Retail View All Cross Industry Solutions AI Agents AI Governance Cybersecurity Marketing Data Migration Professional Services Solution Accelerators Resources Documentation Customer Support Community Learning Training Certification Free Edition University Alliance Databricks Academy Login Events Data + AI Summit Data + AI World Tour AI Days Event Calendar Blog and Podcasts Databricks Blog AI Blog Data Brew Podcast Champions of Data & AI Podcast Resources Documentation Customer Support Community Learning Training Certification Free Edition University Alliance Databricks Academy Login Events Data + AI Summit Data + AI World Tour AI Days Event Calendar Blog and Podcasts Databricks Blog AI Blog Data Brew Podcast Champions of Data & AI Podcast About Company Who We Are Our Team Databricks Ventures Contact Us Careers Open Jobs Working at Databricks Press Awards and Recognition Newsroom Security and Trust About Company Who We Are Our Team Databricks Ventures Contact Us Careers Open Jobs Working at Databricks Press Awards and Recognition Newsroom Security and Trust Databricks Inc. 160 Spear Street, 15th Floor San Francisco, CA 94105 1-866-330-0121 See Careers at Databricks © Databricks 2026 . All rights reserved. Apache, Apache Spark, Spark, the Spark Logo, Apache Iceberg, Iceberg, and the Apache Iceberg logo are trademarks of the Apache Software Foundation . Privacy Notice | Terms of Use | Modern Slavery Statement | California Privacy | Your Privacy Choices
Salary
$228,600 - $342,800
Location
Mountain View, California
Experience
10+ years
Ali Ghodsi
CEO