Senior AI Platform Engineer - Hiring Sprint

About this role

Airbyte is the data and action layer for AI agents. We give agents fast, accurate, authenticated access to business data across hundreds of sources, so they can discover the entities that matter, reason over real-time context, and take action in the systems they read from, not just observe them.

We started as the open-source standard for data movement and proved the economics of data integration at scale: hundreds of connectors, thousands of companies, and, since 2020, have raised $181M from leading investors including Benchmark, Accel, Altimeter, Coatue, and Y Combinator. As our CEO Michel Tricot puts it, "the last ten years were all about structured data. The future is all about context." We're now building that context infrastructure for production-grade agents on the same open foundation, as agents become the primary consumers of enterprise data.

Our mission is unchanged: make data available and actionable to everyone, everywhere. That everyone now includes AI agents.

ENGINEERING HIRING SPRINT:

We're growing our engineering team and are accelerating hiring through a focused Engineering Hiring Sprint. Rather than stretching interviews over several weeks, we're bringing exceptional candidates through an expedited process and making hiring decisions quickly.

Interview process:

1. Apply

2. Technical Take-Home (Java or Python)

3. Hiring Manager Interview

4. In-Person Onsite (the week of July 20)

5. Hiring decision by the end of the week (July 24)

We're hiring across multiple engineering teams, including:

- ⚙️ Platform Engineers

- 🗄️ Database Engineers

- ☁️ Site Reliability Engineers

- 🔌 Extensibility API Engineers

- 🤖 AI Agents Engineers

- 👤 Engineering Managers

If you enjoy solving complex technical problems, moving quickly, embracing AI, and taking ownership of your work, we'd love to meet you.

THE ROLE:

Airbyte is building the runtime that powers trustworthy AI agents. We believe the future isn't simply giving LLMs access to APIs, it's building the infrastructure that allows agents to reason over enterprise context, retrieve evidence, invoke actions safely, and explain every decision they make.

We're building the AI Runtime that sits between language models and enterprise systems. Our runtime resolves entities across hundreds of business systems, assembles the right context, chooses the appropriate connectors and skills, validates evidence, enforces permissions, executes actions safely, and returns responses users can trust.

The interface is becoming dramatically simpler: "Investigate this customer and tell me what changed." Behind that simple request is an intelligent runtime responsible for orchestration, routing, retrieval, evaluation, evidence generation, permissions, and action policies.

We're early. The architecture is taking shape but there's enormous room to define how this works. You'll help build both the runtime infrastructure and the user-facing experiences that make it real.

WHAT YOU’LL DO:

Build the AI Runtime

- Design and implement the orchestration layer that turns natural-language intent into reliable execution.

- Build systems for entity resolution, context assembly, connector orchestration, and evidence retrieval.

- Develop reusable Skills that encapsulate business workflows and domain expertise.

- Build routing systems that intelligently coordinate connectors, tools, deterministic logic, and multiple language models.

Build Products Users Trust

- Own features end-to-end, from runtime capability to user-facing experience.

- Develop evidence-backed reasoning with citations and traceability that users can actually see and verify.

- Build evaluation frameworks that continuously improve quality.

- Implement permission models, freshness validation, and action policies that work transparently for end users.

Ship and Iterate

- Build prototypes, validate ideas, and rapidly iterate with customers.

- Write high-leverage code that enables entire product areas.

- Collaborate closely with Product, Design, Sales Engineering, and Customer Success to turn ambitious ideas into production systems.

- Experiment with new agent architectures while maintaining production-grade reliability.

WHAT YOU’LL NEED:

- 7+ years of software engineering experience building production systems.

- Deep understanding of distributed systems and backend architecture.

- Experience building AI applications using LLMs, agents, RAG, MCP, or modern AI frameworks.

- Strong software engineering fundamentals including APIs, concurrency, testing, and system design.

- Experience building developer platforms, orchestration systems, or workflow engines.

- Ability to rapidly prototype while maintaining production quality.

- Strong product instincts and comfort operating in ambiguous environments.

- Exceptional written communication skills.

- A bias toward ownership and shipping.

NICE TO HAVE:

- Experience building AI agents or agent frameworks, including evaluation systems.

- Familiarity with orchestration frameworks (LangGraph, Temporal, MCP, or similar).

- Experience with retrieval systems, vector search, or knowledge graphs.

- Experience building developer tools or platform infrastructure.

- Experience with data infrastructure including Kafka, Iceberg, Postgres, Spark, or modern data warehouses.

WHAT SUCCESS LOOKS LIKE:

Success isn't measured by the sophistication of the prompts. It's measured by whether the runtime becomes more trustworthy every week. You'll build systems that:

- Automatically assemble the right context and choose the correct connectors and Skills.

- Produce evidence-backed answers with citations.

- Enforce permissions and freshness guarantees.

- Learn from failures through replay and evaluation.

- Allow users to ask once and receive a trusted answer.

As our runtime evolves, users should think less about tools and more about outcomes. The runtim

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