Detect fraud in milliseconds, at the moment a transaction happens, without blocking legitimate customers.

Machine Learning Engineer - Spain

Madrid or Barcelona, Spain · Permanent · Hybrid

What you'd actually work on

Building and maintaining fraud detection and risk-scoring models used on live transactionsDeveloping features from transactional, behavioural, account, and device dataTraining and evaluating models against new and evolving fraud patternsDeploying models into low-latency production systemsReducing false positives while maintaining effective fraud detection ratesWorking with risk specialists to translate fraud scenarios and business rules into model featuresDesigning feedback loops using confirmed fraud cases, manual reviews, and transaction outcomesMonitoring model performance, feature quality, drift, latency, and prediction distributionsInvestigating model degradation and changes in customer or fraud behaviourImproving model deployment, versioning, retraining, and rollback processesDocumenting model behaviour and decisions for engineers, risk teams, and auditorsContributing to code reviews, automated testing, CI/CD, and ML engineering standards

Where it gets technically interesting

Real-time inference under strict latency constraints, with decisions required before transactions are completedHighly imbalanced datasets where confirmed fraud represents only a small proportion of all transactionsFraud patterns that change deliberately in response to existing detection methodsManaging delayed or incomplete labels when transaction outcomes are not immediately knownBalancing fraud detection rates against the commercial and customer impact of false positivesIdentifying drift in models and features before it results in significant financial lossesCombining machine learning outputs with business rules and manual risk controlsMeeting explainability and traceability requirements for decisions that may need to be reviewed laterRolling out new models safely through controlled testing, monitoring, and rollback mechanisms

What we're looking for

3+ years of experience developing applied machine learning modelsStrong Python skills and good software engineering practicesExperience deploying and operating models in productionKnowledge of classification, anomaly detection, or risk-scoring methodsExperience working with imbalanced datasets and appropriate evaluation metricsUnderstanding of precision, recall, false-positive rates, and the business trade-offs between themExperience with model monitoring, drift detection, versioning, and retrainingAbility to work with large transactional or behavioural datasetsExperience with low-latency inference systemsConfidence working with risk, data engineering, platform, and product teamsExperience using Git, code reviews, automated testing, and CI/CD

Previous experience in fraud, payments, insurance, credit risk, or anomaly detection would be valuable, but it is not required if you have worked on comparable production machine learning problems.

The company

A fintech or insurtech scale-up operating across Southern Europe, with several hundred employees and high daily transaction volumes.

The machine learning team works closely with risk and engineering to improve fraud detection while limiting unnecessary friction for legitimate customers.

Health insurance, versátil working, and an equity plan.

Languages: Native or bilingual Spanish and professional English.

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