AI Developer Experience Engineer
Owns PlanetScale's experience with AI coding agents: building and maintaining the MCP server, CLI, and plugin integrations across tools like Claude Code, Cursor, and Codex, measuring agent performance with evals, and teaching best practices through demos, docs, videos, and talks. Requires TypeScript/Go engineering, database experience, and a public communication presence.
- Compensation
- $170K–$220K / yr
- Location
- Remote US
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Developer relations
- Posted
Skills
Must have
- Daily use of AI coding agents, with strong opinions about what makes a tool work well with them
- Experience building an MCP server, plugin, CLI or SDK that real developers use
- Software engineering background in languages like TypeScript and Go, and comfort with APIs, OAuth and the command line
- Experience with writing docs, blogs, and/or video content creation
- Good at communicating with a broad audience and customers alike on socials
- Experience with relational databases (Postgres, MySQL, SQLite), schema design, migrations, connection handling, and Postgres or MySQL in production
- Data fluency: designing evals, writing the queries behind a dashboard, and telling a real trend from noise
- Clear communication of technical ideas in writing, on video and on stage
- Independence and speed: spotting a gap, shipping a fix, and telling people about it
- Genuine curiosity about the AI tooling landscape and a habit of keeping up with it
Nice to have
- Contributions to the MCP spec, SDKs or popular open-source MCP servers
- Experience writing agent skills, plugins or rules files for Claude Code, Cursor or Codex
- Experience with LLM eval tools and building benchmarks for agents
- Experience with AEO or GEO, or with tracking how LLMs surface and recommend a product
- A blog, YouTube channel or GitHub that developers already follow
- Experience with Vitess, database sharding, or running databases at scale
Full description
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 versus CLI usage, and where sessions fail
Show people how to do it well
- Build demos and sample apps that show PlanetScale and AI tooling at their best