Sweden
EuropeAI Ops Engineer (FBOS)
2 дн. назад
SwedenEuropeOnsite
workflow automationsdata pipelinesai tools
Engineer responsible for owning AI-native internal systems automating workflow and data pipelines to support a growing team.
Требования
- Please submit your application in English. It’s our company language, so you’ll be speaking lots of it if you join.
- We treat all candidates equally - if you’re interested, please apply through our careers portal.
Другое
- TL;DR — You'll make sure no one at spends time on anything AI can do. We're defining how AI-native companies operate internally, across Product, GTM, and G&A. You'll own the systems that let a 200-person team move like a 2,000-person company: workflow automations, internal AI tools, data enrichment pipelines. You'll use itself to build much of this. This role is part of Project Automate , working closely with our AI Operations & Automation function and FBOS team.
- is the software creation platform that gives people the power to act on the problems closest to them. For decades, turning an idea into software required so much capital, technical fluency, and time that many ideas never came to life. is the counterargument: a platform for all people with ideas, ambition, and problems worth solving. From solopreneurs to small business owners to teams at companies like Adidas and Zendesk, people have built over 60 million projects on since its launch in November 2024. And we’re just getting started.
- We’re building a generational company from Stockholm, with growing teams in London, Boston, New York, and San Francisco. Our team is small, talent-dense, and moving quickly, with a culture rooted in extreme ownership, high velocity, and low-ego collaboration. We look for people who care deeply, ship fast, and are eager to make a dent in the world.
- is one of TIME’s 100 Most Influential Companies and has been recognized on the Forbes AI 50 and CNBC Disruptor 50, reflecting our momentum as one of Europe’s fastest-growing AI companies and one of the most ambitious places to build in this next era of software.
- Shipped production automations at a company: You've built workflow systems at scale that ran without you. Actual business-critical automations used by teams.
- Engineering brain, modern toolkit: You understand how systems actually work (APIs, databases, architecture) and can review code confidently. You move fast by choosing the right tool for the job: AI agents (Cursor, Claude Code etc) when they're faster, no-code ( itself, n8n, Clay, etc.) when it fits, raw code when it's needed.
- Data is part of your building material: You can turn fragmented data across tools, databases, and external sources into reliable, usable systems. You’re comfortable designing data models, writing complex SQL, building enrichment and transformation pipelines, and creating the feedback loops that keep data trustworthy over time.
- AI tool obsessed: You're always testing the latest models, apps, and workflows before anyone else. You've deployed internal AI tools (Claude/ChatGPT with custom contexts, AI agents, MCPs, etc.) at a company and you're constantly experimenting with new ones.
- Can't unsee inefficiency: You see the whole company as one machine. Once you spot a broken process, you can't leave it alone.
- High agency: You don't wait for permission. You find the bottleneck, build the fix, and ship it.
- Own internal AI systems that make run faster than it should.
- Define how AI-native companies operate: Help build the playbook. What we learn here scales to every company using .
- Build internal tools and workflows: Using , AI agents, no-code, raw code—whatever gets the job done fastest. on first, always.
- Turn company data into operational leverage: Connect and enrich data across our stack, then put it directly to work, powering automations, internal tools, AI agents, decision-making, and proactive workflows across Finance, Business Operations, Legal, etc.
- Deploy AI agent armies: Claude/ChatGPT with MCPs configured so every teammate has AI working for them, not the other way around.
- Squeeze value from our stack: Make the AI features in Slack, Linear, Notion, Ashby, etc. actually deliver.
- Level up the team: Turn everyone at into AI power users. Document what works. Scale it.
- Own it end-to-end: You build it, you maintain it, you improve it.