Product Builder

Product builder Global SaaS | $28M Series B Investment Remote in Europe The Mission Most product ideas die in the gap between "someone should look at this" and "a squad has capacity next cycle. " As a Product Builder, you close that gap by being the whole loop yourself: you find the problem, talk to the users, build the thing, ship it, and read the result. You own the path from problem to outcome. Not just the spec. Not just the ticket. The whole thing. This is not a Product Manager who writes some code, and it is not an engineer who sits in on discovery calls. It's a single role that holds discovery and delivery in the same head, and uses AI to make that possible at a speed a three-person trio can't match on exploratory work. You'll work in the messy, high-ambiguity spaces around our Core Platform Product, our AI Voice Agents, Calling Apps and our product-led surfaces - the friction nobody owns, the repeated support question, the idea that's too early for a squad to commit a cycle to and too valuable to drop. Our empowered squads own our committed roadmap and they're good at it. You own the things that haven't earned a squad yet. A good week for you isn't "I shipped a lot. " It's "we now know something we didn't know on Monday, and there's working software proving it. " Key Responsibilities 1. Own the problem before anyone has written it down - Find the friction. You go looking - in support tickets, in churn calls, in the questions that keep landing in #product, in the gaps between two teams' surfaces. You don't wait for a brief. - Sharpen it into a hypothesis. Vague frustration becomes a falsifiable statement with a metric attached, fast enough that the sharpening doesn't become the project. - Talk to users yourself. Continuous discovery is not a phase you schedule. You're in contact with real customers weekly, and you can run an interview that surfaces behaviour rather than feature requests. - Run several bets in parallel. You explore three or four opportunities at once, then converge on the one or two worth real investment. 2. Build and ship it - End-to-end delivery. Prototype, build, test, ship to production, instrument.

You work across the stack and you make your own design calls - there is no PM or Designer assigned to you, though both are a Slack message away and you use them well. - Ship small and early. You'd rather have something imperfect in front of five real customers this week than something complete in front of nobody next month. - Hold the quality line. Shipping fast is not shipping sloppy. You write tests, you think about failure modes, and you know when a prototype must not be allowed to become production. - Build AI-native, not AI-bolted-on. LLM-driven flows, agentic workflows and orchestration - you understand the models well enough to design around what they actually do, including what they get wrong. 3. Read the result and decide - Instrument before you ship. You define what would prove you wrong before the code goes out. - Kill your own ideas. You can look at a two-week build, see it didn't move the number, and shut it down without needing anyone's permission or a retro to justify it. This is the hardest part of the job and the one we'll dig into hardest in interviews. - Hand off deliberately. When something works and needs to scale, you write it up, transfer it to the owning squad, and walk away. You do not accumulate a personal estate of unowned production services. 4. Work AI-first, and make it count - AI as a multiplier, not the point. You use agentic workflows to compress the loop from idea to shipped. You can explain which tool for which job and why, and you know when the agent is confidently wrong. - Systems, not prompts. Repeatable workflows for research synthesis, prototyping, and experiment readouts - not one-off chats. Validation designed in. - Raise the floor. The patterns you find get written down and d.