AI Developer Experience Engineer

Role overview

Improve how developers use a database platform through AI coding assistants, agent frameworks, and related tools. This hands-on role spans integration engineering, testing and measurement, and public technical education, working with teams responsible for the MCP server, command line tools, APIs, and documentation.

Responsibilities - Build and maintain marketplace integrations, plugins, skills, and listings for AI tools, IDEs, agent platforms, and app builders. - Test the MCP server and command line interface across coding assistants and editors, identify regressions, and resolve friction in database setup, connections, migrations, and query debugging. - Improve the context available to agents through tool descriptions, skills, documentation, error messages, and agent-oriented reference files. - Create evaluations and benchmarks using real agent tasks, and track usage and outcomes such as setup completion, connection time, tool choice, and failure points. - Assess whether AI assistants accurately surface the platform in database recommendations. - Create sample apps and demonstrations, and publish guides, blog posts, and videos; represent the work in conferences and other public forums.

Requirements - Regular use of AI coding agents and informed judgment about what makes developer tools work well with them. - Experience building a developer-facing MCP server, plugin, CLI, or SDK. - Software engineering background, ideally with TypeScript or Go, and comfort with APIs, OAuth, and command line workflows. - Experience with relational databases such as PostgreSQL, MySQL, or SQLite, including schemas, migrations, and production usage. - Ability to design evaluations, write queries for reporting, and distinguish meaningful patterns from noise. - Strong technical communication skills across writing and presentations; experience creating documentation, blog, or video content.

Nice to have

Contributions to MCP standards, SDKs, or open source servers; experience writing agent skills or plugins; familiarity with LLM evaluation, benchmarks, or answer engine optimization; an established technical audience; or experience with Vitess, sharding, and large-scale databases.

Benefits and work setup

The listed base salary range is $170,000–$220,000. The source describes total compensation as including base pay, possible variable compensation, benefits, and equity, with details available during recruiting.