Staff Backend Engineer, Agent Infrastructure

📋 Job Overview

Airbyte is 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.

🏢 About Airbyte

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.

🎯 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.

✅ Key Responsibilities

• 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.

📌 Required Qualifications

• 10+ 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.

⭐ Desirable Experience

• 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.

🎁 Benefits

San Francisco $250K – $280K • Offers Equity. The actual compensation package within the stated range will be based on a combination of factors such as an individual's skills, experience level, location, and other relevant job-related considerations.

🛂 Visa & Eligibility