Applied AI Research Engineer

Summary

Starburst delivers secure, governed access to enterprise data for AI, analytics, and enterprise intelligence. The Applied AI Research Engineer will own the intelligence layer for AIDA's agents, developing grounding systems, retrieval pipelines, evaluation frameworks, and quality metrics while translating research into production systems.

Responsibilities

• 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

Skills

• 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
• 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**: This role will require 25% in-person travel for purposes including but not limited to new hire onboarding, team and department offsites, customer engagements, and other company events. Actual travel expectations may vary by role and business needs

Qualifications

Must Haves

• 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

Nice to Haves

• 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**: This role will require 25% in-person travel for purposes including but not limited to new hire onboarding, team and department offsites, customer engagements, and other company events. Actual travel expectations may vary by role and business needs

Benefits

• All employees receive equity packages (ISOs)
• Comprehensive benefits offering
• Attractive stock grants
• Flexible paid time off