Description CloudLinux is a global remote-first company. We are driven by our principles: do the right thing, employees first, we are remote first, and we deliver high-volume, low-cost Linux infrastructure and security products that help companies to increase the efficiency of their operations. Every person on our team supports each other and does what we can to ensure we all are successful. Check out our website for more information: https://cloudlinux.com/ CloudLinux Inc. develops the Imunify family of security products , designed to protect hosting environments from a wide range of cyber threats. One of these solutions is Imunify Email , an email security product that protects hosting servers against spam, phishing, and other email-based threats. Imunify Email filters mail on about 5,000 hosting servers. Today the filter is rspamd with rules and a scoring model we train in-house. We are replacing it with a transformer we trained ourselves that runs on the customer's own CPU in under 100 MB and already beats rspamd at every operating point we measured. We are looking for a Senior Machine Learning Engineer to join our team and work alongside the engineer currently responsible for both the existing spam-filtering pipeline and the new ML model. Together, you will drive the development, improvement, and production rollout of our email security technology. 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, transfer encodings, SPF / DKIM / DMARC, how Exim, Postfix, and Dovecot handle mail to a filter. - Experience in creating embedded models - Go or Rust for systems code: parsers, streaming, memory budgets, concurrency. - rspamd or SpamAssassin internals: rules, composites, Lua plugins, mass-check scoring. - Multi-million-sample corpora, labeling pipelines, annotator tooling. - Small models for constrained hardware: quantization, distillation, latency budgets. - LLM-based