You make everything the agent runs on solid: the integrations it reaches through, the gateway that turns them into functions it can call, the sandboxes it runs in, and the data underneath. You've built and operated real backend systems in production and have scars from how they break.
You may never have shipped an LLM feature, and that's fine. If you haven't run systems in production, this isn't the role.
Viktor reaches into thousands of integrations for tens of thousands of teams, millions of tool calls a day, on real customer data. Every integration that quietly misbehaves is a task Viktor gets wrong.
We're building a harness ready for AGI-level models: an agent that explores what a company runs on and automates it on its own. That bet lives or dies on the backend underneath. The gateway, sandboxes, and data every task runs through have to be fast, solid, and never the reason the agent got it wrong.
Own integration quality end to end: catch issues before customers do, fix them, and make sure they can't recur.
Build the tool gateway: turn every connected API and MCP into a clean Python SDK the agent composes, with routing, auth, rate limits, and reliability.
Run the sandboxes: a persistent Linux environment per workspace, isolated, secured, autoscaled, and cheap per task.
Own the data layer: state and persistence across tens of thousands of multi-tenant workspaces, durable and fast.
Whatever needs building. Small team, large surface.
You design distributed systems for scale and reliability, and have operated them in production through the breakage.
You know sandboxing and OS-level isolation cold: containers, Linux internals, running untrusted code safely.
You've wrangled real integration plumbing: OAuth, webhooks, rate limits, and third-party APIs that lie.
You think in multi-tenancy, durability, and cost per request, and design for failure by default.
Security and blast radius are instincts you bring to every design, especially when the thing executes model-written code.
You build with AI by default, because that's how the team moves.
You may never have shipped an LLM feature, and that's fine. This is a systems role.
No layers. You work directly with both founders, and decisions get made in the room, not in a Linear ticket.
The backend is the ceiling. Every integration you make reliable and every sandbox you make cheaper raises what the whole agent can do.
You're building the backend of a frontier harness: the layer that decides how much of the model's intelligence turns into real work.
You've worked on infrastructure, platform, or developer-tools teams where reliability was the product.
You've built sandboxing, code-execution, or multi-tenant systems before.
You're at home in agentic engineering workflows, spec to production.
You've contributed to the open-source infrastructure the world runs on.
You've founded something or been an early-stage builder.
Python. No agent framework and no container orchestration layer: Python services, persistent Linux sandboxes per workspace, Modal for infrastructure. You'll live in OAuth and webhooks, the tool gateway, and multi-tenant data. You don't need all of it coming in, but you need to learn fast.
Small team, high trust, low process. Decisions are made by owners, not committees. You will ship your first week. You will talk to users your first day.
We don't do alignment meetings or stakeholder syncs. We build things, see if they work, and iterate.
We're one of the fastest-growing companies in the world. The product works. The market is pulling.
This is a rare window: everyone here owns something real. Not a task. A surface of the company that customers depend on. That doesn't last forever. Right now, it's still true.
Top-of-the-market salary and the kind of ownership that only exists at this stage.
The best work happens when you're in the room. This role is Munich or Warsaw.