Junior Machine Learning Engineer

Our client: Most of the assets they work on resist easy valuation — distressed Spanish loans, repossessed property, the kind of paper the market struggles to price. Our 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 we 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. WHAT YOU'LL BUILD 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. WHAT THEY OFFER Genuine flexibility on where you work: fully on-site in Barcelona or any hybrid split — and the number of days is yours, not a policy. A competitive salary for the Barcelona market, discussed openly. A real learning budget — books, courses, and conferences. A flexible-remuneration plan covering meals and transport. The rarest perk: a domain almost no ML engineer gets to touch, and the scope to own it early. HOW TO APPLY Their process is short and transparent: an initial questionnaire, 30-minute call with the person you would work with, then two or three conversations and a decision within about two weeks. Requirements: 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. Pluses: Document-AI experience (LayoutLM, Donut, TrOCR) or LLM fine-tuning. Spanish or Catalan. Any exposure to finance, legal, or real-estate data. Sof-skills: Curiosity, precision, and clear communication