ppPapaya is building the intelligence layer for vehicle fleets. /p pFleets 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: /p ul liWhat are we actually spending? /li liWhere are we overspending, and why? /li liAre we being charged correctly? /li /ul pThis 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. /p h3What we’re building /h3 pPapaya turns messy fleet data into something companies can trust and act on. /p pAt the core, we: /p ul liIngest real-world data (often CSVs, not clean APIs) /li liStandardise it into a single, consistent model /li liProvide a clear, explainable view of total cost of ownership /li /ul pBut the goal isn’t just visibility. It’s action. We’re building systems that operate directly on the data and take on real workflows:



/p ul liReconciling discrepancies across suppliers when numbers don’t match /li liInvestigating billing issues and drafting dispute cases /li liDetecting anomalies across thousands of vehicles /li liEnforcing policy and contract compliance /li /ul pA 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. /p h3Typical work includes /h3 ul liDesigning and evolving the canonical data model /li liBuilding pipelines that handle messy, real-world inputs /li liDefining how the system resolves conflicting data /li liBuilding and orchestrating agents that reason over the data and take action /li liDesigning how those agents behave, fail, and recover /li liMaking outputs explainable and auditable (not just “LLM says so”) /li liShipping features end-to-end, from idea to production /li /ul h3About the role /h3 pThis is as much a product role as it is engineering. You won’t just implement decisions, you’ll shape how the system behaves. /p h3Benefits /h3 ul liCompetitive salary of €40,000 to €80,000 /li liMeaningful equity /li liApple hardware /li liBarcelona HQ in front of the beach (Norrsken House) /li liGenerous holiday + public holidays /li liTeam off‑site in fun places! (We've been to Girona, Lisbon and Wales so far) /li /ul h3Qualifications /h3 ul liStrong full‑stack ability (stack doesn’t matter) /li liComfortable with ambiguity and messy data /li liAble to take problems from idea to shipped solution /li liProduct mindset: focused on outcomes, not just code /li liStrong opinions, loosely held /li /ul h3Location /h3 pThis role is based in Barcelona. We’re open to candidates relocating from elsewhere in Europe and can support the move for the right person. /p /p #J-18808-Ljbffr

📌 Full Stack Engineer - EU (Barcelona Relocation)
🏢 Papayadash
📍 Barcelona