AI Platform Engineer

Навыки

• AI-агенты
• API (интеграции)
• Google Cloud
• Jaeger / OpenTelemetry
• LLM
• Zapier / Make / n8n
• Мониторинг и observability

О компании и продукте

• Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth.
• We are hiring a AI Platform Engineer to build the execution layer for Supabase's internal AI systems.
• Supabase is building an AI-native internal operating system: a common way of working across the company where AI carries a meaningful share of the operational load rather than sitting alongside it as an assistant. We are standing up a new central team to build those systems, enable the teams, and embed AI operations throughout the organization. You are the engineer on that team.
• The execution layer is yours. You will build the platform that actually runs agents: an event-triggered queue, a headless model-agnostic runtime, durable state so work survives a restart, a human review gate, atomic rollback, and full logging of every prompt, tool call and decision so any run can be reconstructed. You will build the evaluation layer that makes any of it trustworthy, because an agent that cannot be measured cannot be trusted with anything beyond reading. And you will build the agents themselves, across everything from executive reporting down to a layer of agents that watch the platform and improve it.

Задачи

• 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
• HOW YOU'LL THINK
• RECURSIVE THINKING
• You build the generator, not the artifact. When you need thirty agents, you do not write thirty agents
• you build the inventory, the compiler and the distribution path that makes the thirty-first cost an afternoon, then a meta layer whose job is to watch the platform, find its drift and file the fix
• The evaluation layer is the same move applied to trust
• You are not checking whether one agent is correct today

Требования

• 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
• STRONG SIGNAL
• 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
• WHAT SUCCESS LOOKS LIKE
• The platform is boring
• Agents run on a schedule and on events, state survives restarts, failed runs roll back cleanly, and every run can be reconstructed from its log
• Nothing ships unevaluated
• Every registered agent has a real suite with safety cases, the gate blocks regressions, and when someone challenges an output the answer is a test case rather than an argument
• The dangerous action is impossible, not discouraged
• An audit of any agent's credentials shows it cannot perform the writes it is not allowed to perform, and the audit log makes every consequential decision traceable

Условия

• Fully Remote
• We hire globally
• We believe you can do your best work from anywhere
• There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world
• Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together
• Tech Allowance
• Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work
• Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us
• Annual Off-Sites
• Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year
• Flexible Work
• We operate asynchronously and trust you to manage your own time. You know what needs to be done and when
• Professional Development
• Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth

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