Tau leptons play a key role in studying the production of Higgs and electroweak bosons, both within and beyond the Standard Model of particle physics. The precise reconstruction and identification of hadronically decaying tau leptons is essential for present and upcoming high energy physics experiments. Driven by the advancements in jet tagging, we demonstrate that tau lepton reconstruction can be broken down into tau identification, kinematic reconstruction, and decay mode classification using a multi-task machine learning approach. Based on a new publicly available electron-positron collision dataset with full detector simulation and reconstruction, we demonstrate that standard jet tagging architectures can effectively perform these tasks. Our models achieve momentum resolutions of 2–3%, while the accuracy for reconstructing individual decay
modes ranges from 80–95%.