An objective assessment of intrathoracic pressures remains an important objective for patients with heart failure. However, the gold standard for estimating central hemodynamic pressures is an invasive procedure where a pressure transducer is inserted into a great vessel and threaded into the right heart chambers. Approaches that leverage non-invasive signals – such as the electrocardiogram (ECG) – have the promise to make the routine estimation of cardiac pressures feasible in both the inpatient and outpatient settings. In this study, we leverage Deep Metric Learning (DML) to estimate intracardiac pressures from the 12-lead ECG. DML objectives learn embedding that preserves the inherent distance between ECGs where the similar/positive samples lie in the closest representation space. We use dynamic time warping distance between two ECGs to define the positive samples. Our preliminary results show that deep metric learning improves downstream cardiac pressure inference with a limited number of labeled ECGs.