A new loss function, Centroid-Guided Contrastive Loss (CGCL), is proposed for structured fraudulent job posting detection. CGCL unifies classification and clustering to achieve high accuracy and meaningful structure in latent-space representations, capturing subtleties among fake posts. Experiments demonstrate state-of-the-art performance on the EMSCAD benchmark dataset.