AI Platform Engineer
Supabase · Posted Nov 2
An open source Postgres development platform offering hosted database, authentication, and storage services
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About the role
Supabase is the Postgres development platform offering a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. The company is standing up a new central team to build an AI-native internal operating system where AI carries a meaningful share of the operational load. As the sole engineer on the platform, this role builds the execution layer that runs agents (event-triggered queue, model-agnostic runtime, durable state, human review gates, rollback, run logging), the evaluation layer that gates shipping, and the agent portfolio itself.
What you will do
- Ship the agent platform to production. An event-triggered queue, a headless model-agnostic runtime (choosing the runtime is an open decision you will close), durable state that survives a failed run, a human review gate, atomic rollback, and complete run logging in the warehouse.
- Own the evaluation layer, and switch on the gate that depends on it. Golden suites with behavioral assertions rather than intuition, judge criteria with a written rubric, safety cases that must pass on every run, and a CI gate that blocks a regression from merging. This is the precondition for every agent that does anything beyond read internal data.
- Build and register the agent portfolio. Reporting, drafting, linting, triage and question-answering agents across the executive, team-lead and individual-contributor layers, plus a meta layer that observes the platform and improves it.
- Enforce governance in code. Risk tiers the pipeline actually enforces, least-privilege credentials per agent, tool-permission gates, a decision audit log, and autonomy classes where the dangerous class has no code path rather than a warning label. Nothing runs without a registered owner, tier, tool grant and human gate.
- Design how the system contacts people. A hard interruption budget per person, message bundling instead of a stream of pings, and a structure that gives something useful before it asks for anything. Adoption depends on this more than on any other single design choice.
- Own the platform tooling. The compiler and validator, inventory integrity, and the paths that distribute context and capabilities into the repositories and chat surfaces where work happens.
- Compute the operating measures from production data. A pipeline from raw system activity through to a computed maturity grade per team, defensible enough that a team can dispute the result and be answered with the query rather than an opinion.
- Instrument the platform's own return. A ledger that logs the work each agent absorbs and computes the monthly figure, so the value of the system is a measurement rather than a claim.
Skills used in this role
What the employer is looking for
- Have shipped production LLM agent systems that other people depended on. Not demos, not internal showcases. Systems with operational history, real users and at least one incident you can talk through. Prompt engineering on its own does not clear this bar.
- Design evaluations, not spot checks. You build golden sets, write behavioral assertions, define judge rubrics, set pass thresholds and gate CI on the result. You can explain why "we reviewed a bunch of outputs and they looked good" is not evaluation.
- Have done deep API work against the systems work actually lives in, and have authored MCP servers. You know the specific failure modes of those APIs, not just that they exist.
- Own infrastructure end to end in Python on GCP, with a cloud warehouse and infrastructure as code. You provision, deploy, monitor and roll back your own systems, and you close architecture decisions rather than routing them onward.
- Have taste about how software contacts humans. You treat every notification as spending a limited amount of trust.
Preferred qualifications
- Public work in this space. An open-source agent framework, an MCP server, an evaluation harness, or writing on agent reliability that other practitioners cite.
- LLM observability and cost instrumentation. Tracing agent runs, attributing spend per run, and building the queries that turn raw logs into a report someone acts on.
- You have built an internal platform that non-engineers adopted voluntarily, and can describe what you changed after watching them use it.
Benefits and support
- Fully Remote — no Supabase offices, with a WeWork membership or co-working allowance usable anywhere in the world
- ESOP (equity ownership) for every team member
- Tech Allowance to set up your ideal work environment (laptop, monitor, headphones)
- Health Benefits — Supabase covers 100% of health insurance for employees and 80% for dependents
About Supabase
Supabase is an open-source Firebase alternative built on top of PostgreSQL, providing developers with a hosted database, authentication, instant APIs, real-time subscriptions, and storage. The platform simplifies application development by combining relational data capabilities with serverless edge functions and vector search features. Widely adopted by modern developer ecosystems and AI agent tools, Supabase powers scalable backend infrastructure for enterprises and independent builders alike.
- Industry
- Open Source
- Company size
- 350+ employees
- Founded
- 2020
- Location
- Singapore, Singapore
- Funding stage
- Series F
Funding
Series F · $1B+ raised
- 2026-06-04Series F$500M
Leadership
Co-founder & CEO
Co-founder & CTO