{"id":2908204,"url":"https://alion.io/job/thefrenchsourcer-machine-learning-engineer-2","title":"Machine Learning Engineer","company":{"id":4451662,"name":"The French Sourcer","domain":"thefrenchsourcer.com","url":"https://alion.io/company/thefrenchsourcer","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Manatal","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Madrid, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Git","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-10-20T08:38:43Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-26T12:43:15Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Detect fraud in milliseconds, at the moment a transaction happens, without blocking legitimate customers.\nMachine Learning Engineer - Spain\nMadrid or Barcelona, Spain · Permanent · Hybrid\nWhat you'd actually work on\nBuilding and maintaining fraud detection and risk-scoring models used on live transactions\nDeveloping features from transactional, behavioural, account, and device data\nTraining and evaluating models against new and evolving fraud patterns\nDeploying models into low-latency production systems\nReducing false positives while maintaining effective fraud detection rates\nWorking with risk specialists to translate fraud scenarios and business rules into model features\nDesigning feedback loops using confirmed fraud cases, manual reviews, and transaction outcomes\nMonitoring model performance, feature quality, drift, latency, and prediction distributions\nInvestigating model degradation and changes in customer or fraud behaviour\nImproving model deployment, versioning, retraining, and rollback processes\nDocumenting model behaviour and decisions for engineers, risk teams, and auditors\nContributing to code reviews, automated testing, CI/CD, and ML engineering standards\nWhere it gets technically interesting\nReal-time inference under strict latency constraints, with decisions required before transactions are completed\nHighly imbalanced datasets where confirmed fraud represents only a small proportion of all transactions\nFraud patterns that change deliberately in response to existing detection methods\nManaging delayed or incomplete labels when transaction outcomes are not immediately known\nBalancing fraud detection rates against the commercial and customer impact of false positives\nIdentifying drift in models and features before it results in significant financial losses\nCombining machine learning outputs with business rules and manual risk controls\nMeeting explainability and traceability requirements for decisions that may need to be reviewed later\nRolling out new models safely through controlled testing, monitoring, and rollback mechanisms\nWhat we're looking for\n3+ years of experience developing applied machine learning models\nStrong Python skills and good software engineering practices\nExperience deploying and operating models in production\nKnowledge of classification, anomaly detection, or risk-scoring methods\nExperience working with imbalanced datasets and appropriate evaluation metrics\nUnderstanding of precision, recall, false-positive rates, and the business trade-offs between them\nExperience with model monitoring, drift detection, versioning, and retraining\nAbility to work with large transactional or behavioural datasets\nExperience with low-latency inference systems\nConfidence working with risk, data engineering, platform, and product teams\nExperience using Git, code reviews, automated testing, and CI/CD\nPrevious 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.\nThe company\nA fintech or insurtech scale-up operating across Southern Europe, with several hundred employees and high daily transaction volumes.\nThe machine learning team works closely with risk and engineering to improve fraud detection while limiting unnecessary friction for legitimate customers.\nHealth insurance, flexible working, and an equity plan.\nLanguages: Native or bilingual Spanish and professional English.\nA 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