Junior Machine Learning Engineer

We are seeking a JUNIOR MACHINE LEARNING ENGINEER for a StartUp specialized in Financial Asset in Barcelona.

This is a stable position with a permanent contract directly with the StartUp (we will handle the selection process). The salary range could be 25.000 – 30.000 euros per year, gross, including fixed salary and bonus, depending on experience.

They offer flexibility on where you work: fully on-site in Barcelona (Sarriá – Sant Gervasi), or any hybrid split — and the number of days is yours, not a policy.

They offer also a real learning budget — books, courses, and conferences.

A flexible-remuneration plan covering meals and transport.

Their edge is a proprietary corpus of Spanish judicial and financial documents that no foundation model has ever seen, and the machine-learning systems they build on top of it. They are a ~25-person firm that analyzes and advises on Spanish real estate and distressed debt (NPL/REO), and two of those systems sit at the center of the business.

This is a junior role with the reach of a much larger one. As an early, high-impact hire, you would work across both systems — modeling and document AI — as one connected role, not two jobs. What you build shapes the analysis they stand behind and reaches real decisions in days, not quarters. You would work shoulder-to-shoulder with a sharp, multidisciplinary team: engineers, analysts, and specialists in finance and law.

Responsibilities:

-Pricing what almost nobody can price: Predictive and quant models across thousands of Spanish loan and property records — asset valuation, portfolio-performance forecasting, feature enrichment, tabular and time-series modeling. The numbers you produce guide real capital, so evaluation and calibration are where the craft lives.

-Teaching a model to read a courtroom.

-Legal and judicial documents — multi-column, table-dense PDFs where layout and position carry meaning, not flat text — turned into clean, structured data. OCR, layout-aware extraction, NER, and LLM fine-tuning and evaluation. You would weigh the trade-offs that decide whether extraction is dependable or merely demo-ready: OCR + LayoutLMv3 versus OCR-free approaches (Donut, TrOCR) versus vision-language models.

-You would own work across both systems end to end — training, evaluation, deployment, and monitoring, in production rather than in notebooks. It is an unusual amount of scope for an early-career engineer, and you will grow into more of it quickly.

WHAT THIS ROLE IS, AND IS NOT

-Real, production machine learning the business runs on — not a research sandbox, and not a thin wrapper around someone else's API.

-Collaborative and consequential: you build alongside talented people on work that matters, not a queue of tickets someone else has scoped.

-Honest about the data: it is genuinely messy, and structuring it is part of the craft — the ambiguity is where the interesting problems live. You will have real infrastructure, experiment tracking, and an evaluation harness to build on.

Requirements:

-Knowledge of Solid Python and a real grounding in machine learning (PyTorch, scikit-learn, pandas, Hugging Face).

-Evidence that you build and finish things, and that you reason carefully about why a model works or fails.

-You do not need years of experience — recent graduates and self-taught engineers are welcome. A degree helps; something real you have built counts for more.

-They are going to ask you for a repository, a Hugging Face model, a thesis or paper, a Kaggle result, or a few sentences on a project and the hardest problem you solved in it. That is what they read first.

-It’s a plus: Document-AI experience (LayoutLM, Donut, TrOCR) or LLM fine-tuning.

-Fluent English, Spanish or Catalan.

-Any exposure to finance, legal, or real-estate data is a plus, but not mandatory.