Papaya is building the intelligence layer for vehicle fleets.

Fleets spend billions every year, but most of that spending 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 building

Papaya 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 model

• Provide a clear, explainable view of total cost of ownership

But the goal isn’t just visibility. It’s an action.

We’re building systems that operate directly on the data and take on real workflows:

• Reconciling discrepancies across suppliers when numbers don’t match

• Investigating billing issues and drafting dispute cases

• Detecting anomalies across thousands of vehicles

• Enforcing policy and contract compliance

A 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 includes:

• Designing and evolving the canonical data model

• Building pipelines that handle messy, real-world inputs

• Defining how the system resolves conflicting data

• Building and orchestrating agents that reason over the data and take action

• Designing how those agents behave, fail, and recover

• Making outputs explainable and auditable (not just “LLM says so”)

• Shipping features end-to-end, from idea to production

This is as much a product role as it is engineering. You won’t just implement decisions, you’ll shape how the system behaves.

• Competitive salary of €40,000 to €80,000

• Meaningful equity

• Flexible working (3 days in office)

• Apple hardware

• Barcelona HQ in front of the beach (Norrsken House)

• Generous holiday + public holidays

• Team off-site in fun places! (We've been to Girona, Lisbon, and Wales so far)

Preferred experience

We’re looking for a Full Stack Engineer who enjoys building 0→1 products and wants to help shape AI tooling for the mobility industry.

• Strong full-stack ability (stack doesn’t matter)

• Comfortable with ambiguity and messy data

• Able to take problems from idea → shipped solution

• Product mindset: focused on outcomes, not just code

• Strong opinions, loosely held

We care about how you think and what you’ve built.

Recruitment process

We keep our hiring process simple and focused on real work.

1. 30 min – Informal intro chat

2. 75 min – Technical pairing session using a real-world problem (no homework)

3. 45 min – Cultural conversation with the CEO