Applied AI Research Engineer at Starburst

Job description

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 evaluation datasets and informs grounding improvements Some of the things we look 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 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 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 Starburst is dedicated to maintaining fair and equitable compensation practices. The salary range provided for this role reflects the minimum and maximum targets for candidates across all U.S. locations and could be inclusive of variable compensation, such as commission or bonus. All employees receive equity packages (ISOs) and have access to a comprehensive benefits offering. Actual compensation packages are determined based on relevant skills, experience, education and training, and specific work location. For more information, connect with the recruiting team or Hiring Manager during the process as they can provide more detailed information about the salary range. Pay Range $215,000 — $270,000 USD Build your career at Starburst All-Stars have the opportunity and freedom to realize their true potential. By building alongside top talent, we’re empowered to take ownership of our careers and drive meaningful change. Anchored in industry-proven technology and unprecedented success, All-Stars are taking on the challenge everyday to disrupt our industry – and the future. Our global workforce is supported by a competitive Total Rewards program that reflects our commitment to a rewarding and supportive work environment. This includes a variety of benefits like competitive pay, attractive stock grants, flexible paid time off, and more. We are committed to fostering an intentional, inclusive, and diverse culture that drives deep engagement, authentic belonging, and an exceptional All-Star experience. We believe that diversity of thought, perspective, background and experience will enable us to own what we do, drive our success and empower our All-Stars to show up authentically. Starburst provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

Requirements

Must-haves

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 evaluation datasets and informs grounding improvements
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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpythonllmsqlci/cd

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Paid Time Off