Software Engineer - AI Tooling Infrastructure
New
S
SupabaseDeveloper infrastructure
Remote, GlobalFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years of experience building and running backend services in production, and have carried on-call for them.
- Required Skills
- DockerPostgreSQLOAuthTypeScriptGoCI/CD
Requirements
- Have 5+ years of experience building and running backend services in production, and have carried on-call for them.
- Write strong TypeScript or Go and be comfortable working in the other language.
- Have designed and operated APIs with real consumers, including versioning, error handling, rate limiting and backwards-compatible changes.
- Know OAuth, OIDC, JWT and API authorization well enough to debug them in production and explain common deviations from specifications.
- Have a good understanding of PostgreSQL and relational databases.
- Be comfortable with CI/CD, Docker, containerized deployments and infrastructure as code.
- Be able to debug requests across services using metrics, traces and logs.
- Have deep experience in at least one area: APIs and auth; operations; or gateways and distributed systems.
- For APIs and auth: have designed permission models, token scopes or OAuth flows for third-party or machine clients and migrated them without breaking users.
- For operations: have run production services, carried the pager, written alerts and runbooks, used SLOs or similar prioritization methods, and reduced risk in shared systems.
- For gateways and distributed systems: have built or run proxies or gateways, or debugged failures across services, and be able to reason about load and degradation.
Responsibilities
- Build and operate production backend services behind Supabase’s AI products, including the MCP server.
- Manage service deployment, scaling, configuration and release.
- Define and track reliability targets, set SLOs with the SRE team, and use error budgets to prioritize fixes.
- Participate in the AI Tooling on-call rotation, respond to incidents, coordinate with other teams, and write follow-ups and runbooks.
- Own authentication and authorization for AI and agent clients, including OAuth apps, scoped tokens and permissions.
- Own observability through metrics, traces, logs, dashboards and alerts.
- Manage capacity and abuse controls for agent traffic, including rate limits and behavior under load or dependency degradation.
- Improve service architecture, including splitting or reshaping services when appropriate.
- Work with Platform and SRE teams on shared code, release processes, infrastructure, alerting and incident practices.
- Contribute to AI Tooling team tools, such as the MCP server, where reliability and security are affected.
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