AI Developer Experience Engineer

Please note that all communications regarding this position will be sent exclusively from an email address ending in @planetscale.com Any correspondence relating to this role will only originate from this domain.

About the role

More and more PlanetScale databases are created, connected and queried by AI agents. Cursor, Claude Code, Codex and a growing list of other tools now sit between developers and their infrastructure.

You will own that experience end to end. You'll make sure PlanetScale shows up everywhere developers build with AI, that our MCP server and CLI work flawlessly in every agent, and that we can measure how well it all works. Then you'll show the world how to do it well through demos, docs, blog posts, videos, and conferences.

This is a hands-on role combining engineering, content creation, and a strong public voice. You'll work closely with the teams that build our MCP server, CLI, API and docs.

The landscape of how developers work with AI changes fast. You'll have the opportunity to move quickly, experiment, and take the lead on the future of AI use with databases and infrastructure.

Make PlanetScale great with AI tools

• Build and maintain the plugins for PlanetScale in every AI marketplace and directory: agent plugins, skill registries, MCP directories, IDE extension stores and AI app builders

• Own our integrations with AI coding tools, agent frameworks and platforms

• Stay current with AI tools as they launch and ensure our customers have a great experience from day one

• Test the PlanetScale MCP server and CLI across Claude Code, Cursor, Codex, VS Code and other clients, and catch regressions before users do

• Find friction in creating a database, connecting, running migrations and debugging queries from inside an agent, then fix it or drive the fix with the owning team

• Improve the context agents rely on: tool descriptions, skills, llms.txt, docs written for agents, and error messages agents can act on

• Attention to AEO (answer engine optimization): make sure agents find and recommend PlanetScale

• Build and maintain skills for working with Postgres, MySQL, Vitess, Neki, and PlanetScale

Measure how well we're doing

• Build evals that run real agent tasks against PlanetScale databases

• Benchmark how agents perform with PlanetScale and work towards continuous improvement

• Track how often AI assistants and agents recommend PlanetScale for database questions, and whether what they say about us is accurate

• Instrument and report on agent usage: databases created by agents, time to first connection, MCP