AI Platform Engineer
Primary stack
Nice to have's
Job description
About the Position
Join Starburst as an AI Platform Engineer to build shared development primitives and SDKs that enhance engineering velocity.
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 platform engineering hire on the team.
Role Summary
You will build the shared development primitives that make multiple product teams faster, with clean abstractions across the Python/JVM boundary. This role is a force multiplier: instead of building product features directly, you build the platform, SDK, and tooling that enables every other engineer on the team to ship faster.
You will own the developer experience for agent development at Starburst, building from 0 to 1 and scaling across teams. Your work is measured by other engineers' velocity, not your own feature output.
Responsibilities
- Design and build an agent development SDK that abstracts common patterns (tool registration, context management, evaluation hooks, prompt management)
- Build the language bridge between Python (AI ecosystem) and JVM (Starburst platform) with type safety, error propagation, and observability across the boundary
- Create developer tooling: local development environments, debugging tools, test harnesses, CI integration
- Define and maintain API contracts between the AI layer and the Starburst platform
- Build shared infrastructure: model gateway, observability pipeline, experiment framework
- Develop unified, product-agnostic components used across multiple products
- Own platform health beyond feature delivery: performance, reliability, developer ergonomics
Requirements
- 5+ years of software engineering experience, with at least 2 years building platform, infrastructure, or developer tooling
- Proficiency in both Python and JVM languages (Java or Kotlin)
- Experience designing APIs and SDKs consumed by other engineering teams
- Strong distributed systems fundamentals: RPC, serialization, error handling, observability
- Experience with containerization, CI/CD pipelines, and cloud infrastructure (AWS preferred)
- Track record of building tools that measurably improve other engineers' velocity
- 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 LLM integration patterns: model APIs, token management, streaming, tool use
- Experience with cross-language interop (gRPC, FFI, language bridges)
- Open-source contributions to developer tooling or infrastructure projects
- Familiarity with A/B testing frameworks and experiment infrastructure
- Experience supporting both research and production workloads on the same platform
- Java agent framework experience (LangChain4j)
We Offer
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.
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Enterprise Intelligence,Data Engineering,AI and Machine Learning
About the Company Starburst is a flexible data platform built on Trino and Apache Iceberg, designed to provide fast, secure access to data across various environments including clouds, on-premises, and hybrid setups. It enables organizations to unify distributed data without requiring data movement, offering a unified context layer for querying and analyzing data. Products, Services & Tech Stack Core Solutions: Enterprise Intelligence Platform Federated query engine Data lakehouse solutions AI data access and decision-making tools Technologies & Frameworks: Trino Apache Iceberg AIDA (AI Data Agents) Project Scope & Target Clients Client Base: Organizations with distributed data across multiple platforms Enterprises looking for fast, governed analytics without centralizing data Engineering Scale: Projects involving high-concurrency analytics Modernization of data platforms using Iceberg and open formats Building scalable data strategies with reduced data movement
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