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.

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Junior Machine Learning Engineer