Applied AI Research Engineer

Excerpt from the original posting

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. Leading global enterprises trust Starburst to fuel AI, analytics, and enterprise intelligence. Learn more at 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 will turn ambiguous notions of "agent quality" into clear metrics, build the grounding systems that connect agent reasoning to verified data, and create the evaluation infrastructure that makes quality a first-class engineering discipline.

You will 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 will own experiments end to end, from hypothesis through production deployment.

As an Applied AI Research Engineer at Starburst, you will:

- 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 e…

→ Starburst · Greenhouse