# AI Platform Engineer

**Company:** [Supabase](https://hotfix.jobs/companies/supabase)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 7+ years
**Skills:** LLMs, Python, GCP, mcp servers, Infrastructure As Code, Agent Frameworks, evaluation harnesses, llm observability
**Posted:** 2026-07-31

> Build and own the production execution and evaluation platform for Supabase's internal AI agents and operating system. Enforce governance through code with risk tiers, durable state, human gates, and comprehensive observability on GCP with Python.

## Job Description

## Responsibilities
- Ship the agent platform to production, including an event-triggered queue, a headless model-agnostic runtime, durable state that survives failed runs, a human review gate, atomic rollback, and complete run logging.
- Own the evaluation layer with golden suites, behavioral assertions, judge criteria with written rubrics, safety cases, and CI gates that block regressions.
- Build and register the agent portfolio for reporting, drafting, linting, triage, question-answering, and a meta layer that observes and improves the platform.
- Enforce governance in code through risk tiers, least-privilege credentials, tool-permission gates, decision audit logs, and autonomy classes where dangerous actions have no code path.
- Design how the system contacts people with interruption budgets, message bundling, and structures that provide value before requesting input.
- Own the platform tooling including the compiler, validator, inventory integrity, and distribution of context and capabilities.
- Compute operating measures from production data, including a pipeline to maturity grades per team.
- Instrument the platform's own return with a ledger that logs absorbed work and computes monthly value.

## Requirements
- Shipped production LLM agent systems that other people depended on, with operational history, real users, and incidents (not demos or prompt engineering alone).
- Designed evaluations including golden sets, behavioral assertions, judge rubrics, pass thresholds, and CI gating.
- Deep API work against systems work lives in and authored MCP servers, knowing specific failure modes.
- Owned infrastructure end-to-end in Python on GCP, with cloud warehouse and infrastructure as code; provision, deploy, monitor, rollback, and close architecture decisions.
- Taste in how software contacts humans, treating every notification as spending limited trust.

## Nice-to-Haves
- Public work in this space such as an open-source agent framework, MCP server, evaluation harness, or writing on agent reliability.
- LLM observability and cost instrumentation, including tracing runs, attributing spend, and building queries from logs to actionable reports.
- Built an internal platform that non-engineers adopted voluntarily and can describe post-launch changes.

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