Rama Rohit Reddy Gangula (Indeed), Vijay Vardhan Alluri (Indeed), Saif Jawaid (Indeed), Dhwaj Raj (Indeed), Udit Jindal (Indeed)
Online job-application funnels are increasingly abused by automated campaigns that flood employers with non-genuine submissions, distorting metrics and eroding platform trust. We report on the first production-scale, defense-in-depth system that detects and mitigates such abuse in real time on Indeed.com, a major job marketplace processing tens of millions of applications each week. Our architecture couples lightweight client-side traps like selector obfuscation, distributed honeypots, browser-trust signals, and Google invisible reCAPTCHA with a multivariate Isolation-Forest anomaly model that operates entirely without labelled data. A novel precision-weighted F1 objective steers threshold selection to minimise user friction while preserving coverage. Deployed globally, the system blocks a significant number of fraudulent applications per day and achieves a 10.23% reduction in suspected abuse volume without degrading legitimate conversion. We detail the layered design, feature engineering, unsupervised modelling, and adaptive mitigation pipeline, and distill lessons for practitioners defending high-throughput, adversarial web services where labelled data are scarce.