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/ Imunify360 is CloudLinux's security suite for Linux web servers: real-time and background malware scanning, automated cleanup, a WAF, runtime PHP protection (Proactive Defense), a database malware scanner, and IP reputation. It runs on hosting providers' shared, VPS, and dedicated servers, so new web-malware campaigns land on our fleet early. We sit on one of the largest labeled corpora of live web-server malware in the industry: files, verdicts, cleanup outcomes, and analyst decisions collected from production hosting servers every day. It already drives our signatures, heuristics, and ML models, and there is far more to get out of it. We are looking for a Senior ML Engineer who will turn that data into detection that ships — classifiers, LLM-assisted triage and generated signatures — and keep exploring new ML/LLM methods against it, measured by the product's false-negative and false-positive rates, not by notebook accuracy. Our dataset focuses on web malware — PHP, JavaScript, HTML phishing kits, infected WordPress and Joomla plugins and themes, database injections — with the context around each file. What you'll work with: Malware samples from production servers, with verdicts and analyst decisions Years of signatures and detection rules Customer-reported false negatives and false positives Runtime behavior of code on live sites Web-attack traffic File metadata and how files spread across servers Cleanup outcomes, including what came back What you will do: - The goal is 10x more malware detected at the same or lower false-positive rate. Hand-written rules and faster pipelines will not get there; models trained on our data might. - Decide which files on a server deserve a closer look, within tight CPU and memory budgets - Tell malicious code from benign at fleet scale and a sensible cost per file - Generate detection signatures automatically from new campaigns - Catch malware that only looks malicious in context, such as phishing pages and script injections - Route each file to the right detection engine - Make cleanup precise so sites stay clean afterwards - Prove each model improves detection before it ships How we work: - You get an experimentation track, GPUs, and a direct line to leadership; in return, you bring numbers, not opinions. - Remote-first, async across UTC−5 to UTC+8. - Decisions are written down (ADR-Lite) and public by default. - Hardware follows evidence: Each training direction starts with dedicated GPUs on our own bare metal; leadership has committed to fund more when the case is made with data.