FULL-STACK ENGINEER New York City HQ ᐧ Full time ᐧ On-site ᐧ R&D ᐧ $160K-$200K + meaningful equity Prior to Ekho, one of the largest retail segments in the world had no checkout button. If you wanted to buy a vehicle online, the best you could do was fill out an “I’m Interested” form and wait for someone to call you back. Found the bike of your dreams at a dealership two states away? You were mostly on your own. Tax requirements, titling workflows, and registration rules vary by state and county. Most dealers didn’t sell across state lines at all—not because they didn’t want to, but because they had no reliable way to do it. Now they can. A buyer finds a bike, clicks “Buy Now,” completes financing and insurance verification online, and gets it delivered to their door in a few days. The whole thing takes about ten minutes. And the dealer doesn’t have to be at their desk (let alone awake) for any of it. The first time one of our dealers woke up to a completed overnight sale, they messaged us: “Oh my God, this is crazy. We just fulfilled a transaction while the whole team was asleep.” We get messages like this regularly now, and they’re no less exciting than the first one was. What made it possible was 18 months of untangling a combinatorics problem disguised as county-specific titling and registration, and integrating with 50 DMVs that still prefer faxes to APIs. That foundation is built. Now we’re putting AI on top of it, expanding into cars, and building the transaction layer that works in-store as well as online. One thing worth saying directly: Anthropic can’t ship something tomorrow that makes this company obsolete. The moat is the foundation beneath the code: the 50-state compliance framework, the DMV relationships, and the legal licenses we’ve secured. That’s not something you can prompt your way around. Unlike most startups right now, we’re not racing against the next model update. WHAT YOU’LL BE BUILDING Last month, Victor https://www.linkedin.com/in/victor-cano-48a06818a/ carried 80% of the load on our AI Sales Agent and shipped it to six paying clients in a single month, including an 83-dealer OEM network that required building an entirely new two-phase conversation architecture. The AI geolocates buyers, surfaces nearby dealers, then permanently locks the conversation to that dealer and rebuilds its entire system prompt with dealer-specific config, inventory, and personality. What made it genuinely hard: the widget injects into third-party dealer websites via Shadow DOM—every site a unique snowflake of Shopify themes and custom JavaScript that breaks in novel ways. Inventory search had to handle fuzzy matching across inconsistent dealer data. The system prompt grew to 13 modular sections, each tracking conversation state across the full buyer journey. All of this was debugged live while onboarding paying customers. With one more strong engineer, we would have parallelized client launches instead of serializing them, and shipped native calendar support, lead assignment routing, and richer tool support including trade-in estimations and service scheduling. The constraint was headcount, not ambition. The harder infrastructure problem underneath all of it: our platform handles transactions where multiple entities interact with the same deal simultaneously: a seller of record, a delivery dealer, a lender, a buyer. Before we can operate on any piece of data, we have to know who’s asking, which entity they’re representing, and what role that entity is playing in this specific transaction. A user with read access to dealer A and write access to dealer B touching a transaction that involves both—what should they see? What actions should they be able to take? Efficient, secure, and user-friendly permissioning for that model is a problem we’re still working through, and it has implications across the entire platform: account and user management, UI components, API and data layer authentication, and core data architecture. WHO YOU’LL WORK WITH Rowan https://www.linkedin.com/in/rowan-mockler/ grew up in South Africa, where his dad owned a used car dealership. Chris https://www.linkedin.com/in/chris-g-howard/ grew up in Atlanta, and was close family friends with some of the largest dealer operators in the Southeast. They met at Stanford, went to see what good looked like at scale (Rowan at Duolingo, Chris at Meta), then went through YC determined to find the most overlooked problem in the largest industry they could. This one—a $2 trillion industry that couldn’t complete a sale online—was the one that stuck. Bongi https://www.linkedin.com/in/bongifleischer/, our VP of Eng, has known Rowan since high school. He turned down several of Rowan’s ideas before finally saying yes to this one. That kind of conviction from someone who knows the founder well enough to say “no” is its own kind of signal. We’re 34 people, mostly in our mid-to-late twenties, with backgrounds across Stanford, YC, BCG, Goldman, and Meta. Nine of us are engineers. We spend four days a week together in our Flatiron office. Nadim https://www.linkedin.com/in/nadim-el-jaroudi-53059090/ has kept every laptop from every job he’s ever had. They’re now racked in a server farm in his apartment running AI agents (before that it was crypto). David https://www.linkedin.com/in/davidreichek/ edits a sci-fi publication online and curates the strangest stories you’ve ever read. Alexis https://www.linkedin.com/in/alexisdrobles/ is working on becoming a DJ and producer (his genre is deep house). Jon https://www.linkedin.com/in/jwdallas/ studied film and posts photos to Slack that make everyone else’s iPhone photography look like a crime. Rodrigo https://www.linkedin.com/in/chousal/ can find the Spanish speakers in any room in New York, which is its own kind of superpower. Mike https://www.linkedin.com/in/mike-cunningham-a28227a/ is our industry vet. He’s in sales, not engineering; but you’d never