  Tau leptons serve as an important tool for studying the production of Higgs
and electroweak bosons, both within and beyond the Standard Model of particle
physics. Accurate reconstruction and identification of hadronically decaying
tau leptons is a crucial task for current and future high energy physics
experiments. Given the advances in jet tagging, we demonstrate how tau lepton
reconstruction can be decomposed into tau identification, kinematic
reconstruction, and decay mode classification in a multi-task machine learning
setup.Based on an electron-positron collision dataset with full detector
simulation and reconstruction, we show that common jet tagging architectures
can be effectively used for these subtasks. We achieve comparable momentum
resolutions of 2-3% with all the tested models, while the precision of
reconstructing individual decay modes is between 80-95%. This paper also serves
as an introduction to a new publicly available Fu{\tau}ure dataset and provides
recipes for the development and training of tau reconstruction algorithms,
while allowing to study resilience to domain shifts and the use of foundation
models for such tasks.
