What you will do

:Train, evaluate, and release spam-detection models: build the corpus, pick the threshold, read the precision/recall report, decide ship or retrain. Build the production side of the new engine in Go: the inference service, the mail-filter integration, the tokenizer, the streaming message parser. Fix detection quality end to end, from a customer's "this phishing got through" to a rule, a corpus fix, or a retrain, with the false-positive cost weighed against the catch. Keep the corpus tooling and training infrastructure running. Review and approve classifier, rule, and model changes, so no release waits on one person. Answer partner engineers who ask why a message scored the way it did. Write specs, handbooks, and post-mortems. That is how this team transfers knowledge.

Your first six months:

Month 1: you ship a rule-model release on your own. Month 3: your first piece of the new engine is merged and running on staging. Month 6: a model release goes out while the principal engineer is away.

Requirements





What you bring:

5+ years of shipping detection or classification systems where false positives have a cost you had to defend: anti-spam, anti-phishing, anti-malware, fraud, abuse. You have taken at least one ML classifier to production yourself: corpus construction, threshold selection, error analysis. Hands-on transformer training in PyTorch: tokenizers, masked-language pre-training, fine-tuning, ONNX export, CPU inference measurement. Linux, shell, and Python on shared servers and a shared GPU. Daily usage of AI coding agents and tools. English at upper-Intermediate (B2) level or higher , with particularly strong written communication skills for technical documentation, specifications, and code reviews. Your specs and merge-request descriptions count as much as your code.

It is a strong plus if you have:

Email internals: SMTP, MIME,

📌 Senior ML Engineer (Madrid)
🏢 CloudLinux
📍 Madrid