About Airbyte

We’re on a mission to make data available and actionable to everyone, everywhere. With 700+ connectors, our platform powers analytics and gives AI agents the business context they need to do real work. More than 25,000 companies, including 20% of the Fortune 500, rely on Airbyte to put their data to work.

We’re built on open source because we believe companies should have full control over their data. A community of 27K members helps shape what we build, and we’re backed by $181M from investors including Benchmark, Accel, Altimeter, Coatue, and Y Combinator.

Models keep getting better. What holds AI back is the data informing and shaping its decisions, and that's the layer we're building.

We’re just getting started. If you’re excited by big challenges, meaningful ownership, and the chance to help build what’s next, we’d love to meet you.

The Role

Airbyte is incubating new product opportunities at the intersection of data, AI, and developer infrastructure. Our small team builds tooling and infrastructure for data practitioners and software engineers, leveraging Airbyte's foundation in enterprise connectivity.

This is not an ML research or prompt engineering role. We're looking for an experienced backend or platform engineer who has built complex production systems and has also worked hands-on with AI applications or infrastructure. You'll own technical explorations, build prototypes, and take promising ideas through to production.

We're still early. Some of our work extends existing products, while other projects begin as hypotheses that need validation. There's significant room to influence technical direction, challenge assumptions, and help shape what we build next.

What You’ll Do

Airbyte MCP: Build infrastructure that lets AI agents configure Airbyte, troubleshoot syncs, and access data directly from connected sources and warehouses. Design reliable, secure, and observable interfaces across the connector ecosystem.

Semantic Search & Retrieval: Build and operate search infrastructure for high-quality retrieval over enterprise data. Improve relevance, latency, and reliability in production.

Semantic Models: Develop systems for defining and maintaining semantic models over enterprise data. Help AI applications use consistent business definitions and produce answers that can be verified against underlying data.

SaaS Sandbox: Build realistic, stateful replicas of SaaS systems for developing and testing agents. Reproduce API behavior, failures, and state changes so workflows can be evaluated without using production accounts.

Explore New Product Opportunities

Turn product hypotheses into focused technical experiments. Build prototypes that test feasibility and expose important constraints.

Work directly with customers, Product, Design, and customer-facing teams to understand real-world problems and incorporate feedback into technical decisions.

Evaluate competing technical approaches and use what you learn to influence product direction.

Build Production-Grade Infrastructure

Design and implement backend systems, APIs, and infrastructure for AI, data, and developer products.

Build reliable, observable systems with clear failure handling, debugging, and operational safeguards.

Take promising prototypes into production and improve them based on usage, incidents, and customer feedback.

Lead Technical Direction

Own architecture and execution across technical design, implementation, rollout, and iteration.

Lead technical decisions that span teams. Align interfaces and dependencies, and influence architecture beyond your immediate projects.

Communicate priorities, risks, and tradeoffs clearly, and follow through on commitments.

What You’ll Need

You may be a strong fit if you are fundamentally a backend or platform engineer who has shipped AI-powered products.

7+ years of software engineering experience building and operating production systems.

Strong backend engineering fundamentals and experience designing distributed systems.

Experience building complex systems involving APIs, asynchronous workflows, concurrency, queues, state, retries, or distributed execution.