The continual emergence and extinction of pathogen lineages, driven by factors including immune escape, environmental changes, or differences in transmissibility, poses major challenges for public health. Detecting lineages with an increased fitness is critical for understanding epidemiological shifts and guiding targeted interventions. Existing approaches for detecting fitness changes among lineages, such as PhyloWave, extract summary statistics from trees and use them to identify lineages with different evolutionary dynamics. However, PhyloWave depends on substantial domain knowledge and thresholds that require manual fine-tuning—often relying on expert judgment or arbitrary choices—which limits its scalability and robustness across different pathogens. To overcome the limitations of existing approaches, we integrate contrastive representation learning with phylogenetic modeling to implement a generalization of the multi-type birthdeath (MTBD) model in which mutation events alter lineage-specific transmission rates.