Applied AI Research Engineer
Starburst · United States
About this role
**Applied AI Research Engineer — Starburst** ## About Starburst Starburst delivers enterprise intelligence at scale by giving organizations secure, governed access to all their data, wherever it lives. Built for distributed data environments, Starburst helps enterprises power AI and analytics without the cost and complexity of traditional data consolidation. With open standards including Trino and Apache Iceberg, Starburst enables trusted access to complete enterprise context while helping organizations avoid vendor lock-in. Learn more at http://starburst.ai. ## About the Team We build the AI layer for Starburst’s products, including AIDA. We design agents that let users ask questions in natural language and get accurate, grounded answers backed by their actual data. We operate with startup speed inside an enterprise company—shipping weekly and measuring results. This is the first dedicated research engineering hire on the team. ## Role Summary You will own the intelligence layer that makes AIDA’s agents correct, trustworthy, and measurably better over time. The work spans information retrieval, knowledge representation, and evaluation science. You’ll turn ambiguous notions of “agent quality” into clear metrics, build grounding systems that connect agent reasoning to verified data, and create evaluation infrastructure that makes quality a first-class engineering discipline. You’ll operate at the research/systems boundary: running experiments with academic rigor and shipping results with production engineering discipline. Research and engineering are not separate tracks here—you’ll own experiments end to end, from hypothesis through production deployment. ## What you’ll do - Design and build grounding systems that connect agent reasoning to verified enterprise data sources - Build and optimize retrieval pipelines (RAG, hybrid search, structured query generation) for accuracy and latency - Define data representation strategies that preserve semantic fidelity across heterogeneous enterprise data (catalogs, schemas, lineage) - Create evaluation frameworks: automated benchmarks, regression suites, human evaluation protocols - Convert validated research findings into production systems that ship to users - Establish quality metrics and dashboards that track agent correctness week over week - Build feedback loops where user interaction data flows back into evaluation datasets and informs grounding improvements ## What we’re looking for - 3+ years of experience in information retrieval, NLP, knowledge representation, or applied ML research - Production experience building RAG, grounding, or retrieval systems (not prototypes or demos) - Strong evaluation methodology: benchmark design, statistical analysis, reproducible experiments - Comfort operating at the research/systems boundary: you read papers and you ship code - Python fluency; experience with vector databases, embedding models, LLM APIs - Track record of converting research insights into shipped production systems ## Preferred Qualifications - Experience with enterprise data systems (SQL engines, data catalogs, schema metadata) - Familiarity with text-to-SQL or structured query generation - Published research or open-source contributions in IR, NLP, or evaluation methodology - Experience designing evaluation pipelines that run in CI/CD - Familiarity with JVM-based systems - **Ability to Travel**: ~25% in-person travel for onboarding, team/department offsites, customer engagements,
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