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About This Role
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
- Write docs, guides and blog posts on using databases effectively with AI, including safe access, branching and performance assessment
- Make videos and attend conferences to communicate with our audience how to build effectively with AI
These attributes best describe you
- 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 working 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
Bonus points
- 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
Why PlanetScale
We're redefining how high-growth companies manage data at scale—and we work with some of the most exciting brands in gaming, consumer tech, and B2B SaaS. As a Software Engineer, you'll be at the core of building the platform that powers world-class apps used by hundreds of millions of users worldwide. PlanetScale is a profitable company with a philosophy centered around building small teams of p99 individuals and is recognized as one of the fastest growing companies in America.
At PlanetScale we believe in supporting people to do their best work and thrive no matter the location. Our mission is to build a diverse, equitable, and inclusive company. We strive to build an inclusive environment where all people feel that they are equally respected and valued, whether they are a candidate or an employee. We welcome applicants of any educational background, gender identity and expression, sexual orientation, religion, ethnicity, age, citizenship, socioeconomic status, disability, pregnancy status, and veteran status.
If you have a disability, please let us know if there's any way we can make the interview process better for you; we're happy to accommodate!
Total Compensation and Pay Transparency
An employee's total compensation consists of base salary + variable comp where appropriate + benefits + equity. A member of our Talent Acquisition team will be happy to answer any further questions when we engage with you to begin the interview process.
Base Salary Range: $170,000 - $220,000
Salary Context
This $170K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At PlanetScale, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($195K) sits 8% below the category median. Disclosed range: $170K to $220K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
PlanetScale AI Hiring
PlanetScale has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $220K - $220K.
Remote Work Context
Remote AI roles pay a median of $188,200 across 2,430 positions. About 6% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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