Software Engineer - AI Tooling
New
S
SupabaseAI developer tooling
Remote, GlobalFull-TimeMiddle
SalaryEvery team member receives ESOP (equity ownership) in the company.
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Job Details
- Experience
- 5+ years of experience in professional software engineering experience.
- Required Skills
- JavascriptTypeScriptPostgres
Requirements
- Have 5+ years of professional software engineering experience.
- Write strong TypeScript and know the JavaScript ecosystem, including npm, ES modules, bundlers, and compatibility.
- Be familiar with current AI tools, agent patterns, and practical trade-offs.
- Have built developer tools, product interfaces, or platform primitives used by other engineers.
- Have designed or used evaluation frameworks to measure AI quality, reliability, and task success, with an understanding of baselines, variance, and cost.
- Use sound judgment about where AI improves workflows and how to avoid fragile or gimmicky experiences.
- Write tests and validate reliability in real-world conditions.
- Know Postgres and relational database practice.
- Communicate clearly in writing and work across product, engineering, and documentation.
- Be comfortable navigating ambiguity and moving quickly.
Responsibilities
- Build and improve the MCP server, agent skills, agents, and Assistant.
- Create tools, skills, and evals that make new Supabase products usable by agents.
- Prototype tooling for emerging AI technologies and evaluate whether it should go to production.
- Build evals, instrumentation, and feedback loops; measure tools against a no-tool baseline and help define quality KPIs and regression gates.
- Use customer and agent usage to improve tooling, interfaces, and documentation.
- Design how documentation and product knowledge are exposed to agents, and measure effectiveness.
- Build patterns and tools that help customers add AI to their applications, including MCP on Edge Functions, vector embeddings, and agents running on Edge Functions.
- Enable contributions to AI Tooling through contribution paths, templates, documentation, and review practices.
- Centralize shared AI logic and ship tools with testing, benchmarking, and stress testing.
- Work with AI Tooling Infrastructure engineers on reliability, auth, and hosting, and collaborate with Docs, CLI, Frontend, and SDK teams.
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