Documentation Engineer

Role overview Make technical data infrastructure easier for developers to understand, adopt, and use in real applications. This role combines documentation engineering, hands-on software work, and developer

experience: you will create clear guidance and working examples, design documentation that AI agents can follow, and build checks that keep content reliable as the product changes.

Responsibilities - Write and maintain quickstarts, tutorials, conceptual guides, API references, and troubleshooting material. - Structure guidance for both people and AI agents, with clear prerequisites, complete examples, and recovery steps; evaluate it through task-based trials. - Build runnable examples and sample applications covering ingestion, vector search, filtering, indexing, and multimodal data workflows. - Use AI coding tools to research code, draft content, and automate maintenance, then verify results against actual product behavior. - Maintain tooling and automated checks for examples, references, links, and content quality. - Work with engineering, product, and open-source contributors to document releases, improve navigation, address user feedback, and support contribution practices.

Requirements - At least eight years in documentation engineering, developer-focused technical writing, software engineering, or a related field. - A portfolio demonstrating clear technical explanations that help developers complete real tasks. - Experience designing documentation for AI-agent use and assessing how structure and context affect task completion. - Strong Python skills, including the ability to write, run, and debug SDK or API examples. - Familiarity with databases, SQL, and data workflows, plus Git, pull requests, Markdown, and documentation in software development workflows. - Ability to learn by reading code and experimenting with APIs, with strong editorial judgment, technical accuracy, and independent prioritization.

Nice to have Experience with database, data infrastructure, AI, or machine-learning documentation; vector search or multimodal systems; TypeScript or Rust; generated API references and CI-based example checks; agent workflows; or open-source communities.