Airbyte Staff Backend Engineer at Airbyte building infrastructure and tooling for AI applications and data connectivity. Owns technical explorations and prototypes to advance new product opportunities.
Responsibilities
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.
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
Qualification
Experience with orchestration systemsExperience with retrieval systemsExperience building developer toolsWhat Success Looks Like
Required
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.
Hands-on experience building and shipping AI-powered products using LLMs, agents, RAG, tool calling, MCP, or similar technologies.
Experience with orchestration systems, workflow engines, developer platforms, or infrastructure products.
Strong system design skills and the ability to reason about reliability, scale, observability, and failure modes.
Demonstrated ability to lead technical direction across teams and influence architectural and engineering decisions.
Ability to move quickly from prototype to production without sacrificing engineering quality.
Strong product instincts and comfort operating in ambiguous, 0-to-1 environments.
Exceptional written communication skills.
A strong bias toward ownership and shipping.
Preferred
Experience building and operating AI agents in production, including debugging, observability, and evaluation.
Experience building agent infrastructure, agent platforms, or evaluation systems.
Experience with retrieval systems, vector search, search infrastructure, or semantic models.
Experience building developer tools, platform infrastructure, or integrations with third-party SaaS APIs.
Experience with data infrastructure such as Kafka, Iceberg, Postgres, Spark, or modern data warehouses.
What Success Looks Like
Success is not measured by the number of prototypes we build. It is measured by whether we turn promising ideas into products customers use and infrastructure they can depend on.
Take new ideas from prototype to production and improve them through customer feedback and real-world usage.
