Sr. Software Engineer, Fullstack

About the Role

This role offers a unique opportunity to shape how Zapier understands and leverages integration data, influencing every AI product we build, and be on the leading edge of AI software products. Join the Knowledge by Zapier team to design intuitive systems, work across the stack, and enable automation at scale.

Responsibilities

• Design, scale, and maintain internal tools, APIs, and user interfaces that unlock the power of files, data, and knowledge across thousands of integrations.
• Work primarily in backend systems, with the ability to contribute to frontend code (React, TypeScript) as needed.
• Partner closely with AI and infrastructure teams to power data syncing, search, structured outputs, and test automation.
• Lead technical initiatives, mentor teammates, and contribute to architectural decisions that set long-term direction for the product.
• Help define and implement improvements to our developer-facing surfaces, including dashboards and docs.

Requirements

• Strong backend experience using Python (Django preferred), Node.js, or similar frameworks.
• Familiarity with frontend tools like React and TypeScript, and a willingness to contribute as needed.
• Experience designing and consuming REST APIs, event-based systems, and scalable backend workflows.
• Comfort working across cloud environments, CI/CD pipelines, and observability stacks.
• You value collaboration, pairing, and async communication in a remote team environment.
• You’re customer-focused, caring about solving real problems and creating great experiences.
• You love learning and are curious about ambiguous or novel problems, comfortable asking questions and figuring things out.
• You’ve used AI tooling for work or personal use, or are willing to dive in and learn fast, eager to deepen your understanding of AI and use it regularly.

Nice-to-Haves

• RAG Background: Experience building or optimizing Retrieval-Augmented Generation (RAG) systems, including designing data pipelines, working with vector databases, or implementing semantic search and structured retrieval to make knowledge more accessible to AI models.
• LLM experience: Experience building or supporting LLM-powered products in production.
• Feedback & Telemetry: Familiarity with collecting telemetry or usage metrics to inform product improvements.
• Cross-Codebase Contributions: Comfort navigating and contributing across multiple services and codebases in a large, distributed system.