Papaya is building the intelligence layer for vehicle fleets.Fleets spend billions every year, but most of that spend is poorly understood. Data is fragmented across leasing providers, fuel, maintenance, telematics, and charging. There’s no single source of truth. Costs don’t reconcile. Decisions are reactive. Even large organisations struggle to answer simple questions:What are we actually spending?Where are we overspending, and why?Are we being charged correctly?This has always been a hard problem. What’s changed is that it’s now solvable. We have real customers, real fleet data, and a clear industry need. At the same time, the AI tooling needed to make sense of this data is just becoming viable. The next generation of fleet software won’t just store information. It will understand what is happening, explain why it matters, and help teams act.What we’re buildingPapaya turns messy fleet data into something companies can trust and act on.At the core, we:Ingest real-world data (often CSVs, not clean APIs)Standardise it into a single, consistent modelProvide a clear,



explainable view of total cost of ownershipBut the goal isn’t just visibility. It’s action. We’re building systems that operate directly on the data and take on real workflows:Reconciling discrepancies across suppliers when numbers don’t matchInvestigating billing issues and drafting dispute casesDetecting anomalies across thousands of vehiclesEnforcing policy and contract complianceA big part of this is building and orchestrating agents that can do this work reliably and that our customers can trust. You’ll work across the full stack on problems where data is incomplete, inconsistent, and sometimes wrong.Typical work includesDesigning and evolving the canonical data modelBuilding pipelines that handle messy, real-world inputsDefining how the system resolves conflicting dataBuilding and orchestrating agents that reason over the data and take actionDesigning how those agents behave, fail, and recoverMaking

📌 Full Stack Engineer (Madrid)
🏢 Papayadash
📍 Madrid