The first conversation at Lovable is usually with a founder, and it does not feel like an interview. Candidates describe a loose talk with Anton Osika, or someone who reports straight to him, about what you have built from nothing, why AI coding tools are eating the bottom of the software market, and what you would do with an empty repo and one week. No whiteboard, no algorithm trivia. Treat it as a warm-up and you have already failed the round, because that chat is where they decide whether you have the zero-to-one instinct the whole company is built around.
A little context explains why they screen that way. Lovable came out of Stockholm in late 2024, built by Osika and Fabian Hedin, and turned plain-English app generation into one of the fastest revenue ramps the industry has seen. Its annualized revenue went from around $200M to a run rate past $500M over 2026, and its valuation moved from $6.6B at the end of 2025 to a number reported above $13B by mid-2026. The headcount behind those figures is small, roughly a couple hundred people, and the company is hiring hard right now with a blunt pitch to engineers getting cut from Big Tech: come somewhere your code ships the day you write it. That ratio of revenue to people is the reason the bar sits where it does.
The founder chat filters harder than it looks
The opening round rewards specifics. If you say you “led a project,” they will ask what you personally wrote, what you deleted to hit the deadline, and what broke in production because of a call you made. Fuzzy ownership reads as a red flag. The engineers who pass tend to talk about things they shipped alone, the exact moment they cut scope, and a technical decision they would defend even though it was ugly. Have two or three of those stories ready with real detail, including the parts that went sideways.
Fit gets tested quietly in the same conversation. Lovable moves fast and expects people to argue about the work without making it personal. If you come across as someone who needs a spec handed to you, or who treats a disagreement as an attack, the round ends politely and leads nowhere.
The live coding round is a reading test as much as a writing test
After the founder screen come one or two technical rounds, usually in TypeScript, sometimes Python for backend or ML-adjacent work. These are not hard algorithm puzzles. The format is closer to a real working session: you get a small feature to build or a broken component to fix, live, while you talk through the tradeoffs. What they watch is how you move through code you did not write, how you form a hypothesis about a bug, and whether you check your assumptions instead of guessing.
A frequent version hands you a React component that misbehaves on fast input and asks you to find the problem. The bug is often a stale closure or a race between async responses, the kind of thing that shows up constantly in a product built on streaming output. Something like this:
useEffect(() => {
let active = true
fetchPreview(prompt).then(r => {
if (active) setPreview(r) // drop responses that arrive out of order
})
return () => { active = false }
}, [prompt])If you spot that the old request can resolve after the new one and overwrite fresh state, and you can explain the cleanup guard without hand-waving, you are most of the way there. Saying “I’d write a test for this first” and then actually writing a small one lands well, because it matches how they work day to day.
System design for an AI product, not a generic feed
Senior candidates get a design round, and it is refreshingly on-topic. Instead of “design Twitter,” you get something the company actually lives with: design the system that takes a user’s prompt, generates a full-stack app, previews it live, and deploys it, for thousands of concurrent users. The interesting parts are the ones specific to this product. How do you stream generated code to the browser as the model produces it. How do you run untrusted generated apps in a sandbox without letting one user’s code reach another’s. Where does inference cost blow up, and what do you cache to keep it in check.
Bring up prompt caching, sandbox isolation, and graceful handling of a half-finished generation, and you are speaking their language. A strong answer names the failure modes out loud: a model that returns broken code, a preview environment that hangs, a deploy that only half-completes. They want to see you reason about cost per request, because at their volume inference spend is a real line item and not an afterthought.
The round structure, and what each stage is really testing
| Stage | Format | What they are really testing | How to prepare |
|---|---|---|---|
| Founder or hiring-manager chat | 30-45 min conversation, no coding | Zero-to-one instinct, real ownership, why you want a fast startup | Prepare two or three stories about things you shipped solo, including what you cut and what broke |
| Live coding | 60 min, TypeScript or Python, build or debug | Reading unfamiliar code, debugging under light pressure, testing habits | Practice fixing React and async bugs while narrating your reasoning; write a quick test unprompted |
| System design (senior roles) | 60 min, shared doc or whiteboard | Designing around LLM generation, sandboxing, and inference cost | Be ready to design a code-generation and live-preview pipeline, not a generic social feed |
| Values and team fit | 45 min behavioral | Bias for action, ego-free disagreement, comfort with high autonomy | Have examples of moving without a spec and changing code you did not own |
Questions phrased the way they actually ask them
- “Walk me through something you built from zero. What did you cut to get it out the door?”
- “This component flickers when you type quickly. Find the bug and fix it.”
- “Design the system that runs generated apps safely for thousands of people at once.”
- “Tell me about a time you changed code you didn’t write and were afraid of breaking it. What did you do?”
What the offer tends to look like
Pay is competitive with senior Big Tech, but the shape is different. Cash is solid without trying to beat FAANG on base alone, and the weight sits in equity priced against a valuation that has moved fast in one direction. That is the real bet you are making: options set against a company that has repriced upward several times in under two years, with the ordinary risk that the number stops climbing. Stockholm roles and remote roles are structured differently on base and equity, and Swedish option taxation is its own subject worth reading up on before you sign anything.
Do not anchor on a headline figure from a blog post. Ask for the strike price, the current preferred valuation, the vesting schedule, and how they handle refreshers, then check comparable senior offers on a site like levels.fyi instead of guessing. Because the company is young and the cap table is still moving, the gap between a good grant and a mediocre one lives in details they will give you if you ask directly.
Who actually gets through
The people who do well here are usually the ones who have already run something small and end to end, whether that is a startup, an open-source project with real users, or an internal tool they owned from idea to production. They tend to be comfortable being wrong in front of others and fixing it quickly, and they treat an unfamiliar codebase as something to read carefully rather than complain about. Pure interview athletes who can grind algorithm sets but freeze the moment a problem gets vague tend to stall in the founder round, long before any of that matters.
What to internalize before you apply is that Lovable is not hiring people to work through a backlog of tickets. Every round checks the same trait from a different angle: can you take an ambiguous problem and ship something real, fast, without anyone holding your hand. If that is already how you work, the interview will feel easy and the job will feel like a fit. If it isn’t, no amount of algorithm grinding will paper over the gap, and you’ll feel it in that first founder chat before you ever open an editor.
Practice the behavioral round:
