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 - Email Security (Remote Work) (Madrid)
🏢 CloudLinux
📍 Madrid