AI Tooling Engineer

About the Role

The role requires a combination of product thinking, strong engineering fundamentals, and an eval-first mindset. You will need to understand how customers are using AI tools and translate those workflows into better product experiences, abstractions, and documentation. Collaboration with cross-functional teams, including docs, CLI, frontend, API, and other engineering teams, is essential to deliver coherent developer experiences.

As an AI Tooling Engineer at Supabase, you will have the opportunity to work on a wide range of tasks, from building and maintaining AI tooling to designing and improving the ways documentation and product knowledge are exposed to agents.

What You Will Do

• Build and maintain Supabase's AI tooling, including surfaces such as MCP, agent skills, and other interfaces developers and agents use to work with Supabase.
• Progress Supabase's dashboard assistant into a genuinely useful self-serve product for debugging and day-to-day product workflows.
• Understand how customers are using AI tools and translate those workflows into better product experiences, abstractions, and documentation.
• Drive an eval-first approach by building and improving evaluation frameworks, instrumentation, and feedback loops across real use cases.
• Design and improve the ways documentation and product knowledge are exposed to agents.
• Build tools and patterns customers can use to incorporate AI into their own apps, including areas like MCP on Edge Functions, vector embeddings, and agents running on Edge Functions.
• Centralize shared AI logic and reduce duplicated patterns across our products and services.
• Ensure the tools we ship are battle-tested, with strong test coverage, benchmarking, and stress testing where appropriate.
• Work cross-functionally with docs, CLI, frontend, API, and other teams to deliver coherent developer experiences.
• Stay close to the fast-moving AI ecosystem and make good product and engineering decisions about what is actually useful to builders.

What We Are Looking For

• Strong experience in TypeScript and the JavaScript ecosystem, including npm, ES modules, bundlers, and browser compatibility concerns.
• Deep familiarity with the AI ecosystem, including current tools, agent patterns, and practical tradeoffs.
• Experience building developer tools, product interfaces, or platform primitives used by other engineers.
• Experience designing or using evaluation frameworks to measure AI quality, reliability, and task success.
• Familiarity with Postgres and database best practices.
• Experience with vector databases and embeddings, ideally with pgvector or similar Postgres extensions.
• Strong judgment about where AI improves user workflows and how to avoid fragile or gimmicky experiences.
• Strong focus on writing tests and validating reliability in real-world conditions.
• Strong communication skills and the ability to work cross-functionally across product, engineering, and documentation.

Nice to Have

• Experience with Edge Functions and serverless computing.
• Knowledge of CI/CD pipelines and automated testing frameworks.
• Familiarity with Supabase's technology stack and ecosystem.

Benefits and Perks

• Fully remote work arrangement with a global team.
• ESOP (equity ownership) in the company for all team members.
• Tech allowance to set up your ideal work environment.
• Health benefits, including 100% coverage for employees and 80% for dependents.
• Annual off-sites for team building and collaboration.
• Flexible work arrangement with asynchronous operations.
• Professional development allowance for ongoing education and growth.

• When applying, make sure to highlight your experience with TypeScript and JavaScript, as well as your understanding of AI ecosystems and tooling.
• Showcase your ability to work independently and collaboratively in a remote setting, with strong communication skills.
• Be prepared to discuss your approach to building and testing reliable tools and interfaces, and how you stay up-to-date with the latest developments in AI.
• Emphasize your problem-solving skills and ability to translate complex technical concepts into simple, actionable solutions.
• Consider including examples of your work with evaluation frameworks, instrumentation, and feedback loops, and how these have improved product experiences.
• Be ready to talk about your experience with Postgres, vector databases, and embeddings, and how these relate to your work in AI tooling.