Accurately predicting Remaining Useful Life (RUL) provides significant benefits - it increases safety and reduces financial and labor resource requirements. Relying on just one methodology for RUL prediction is unsuitable because certain methods of prediction perform better for certain use cases and conditions. Approaches must be combined to maximize accuracy. Encoding these hybrid methods is challenging because their models are complex, change frequently, and represent a wide range of devices, components, and systems. The algorithms associated with these models also require deep mathematical understanding. We propose using probabilistic programming (PP) to integrate physical models and data-driven methods into a probabilistic model that can predict RUL under the Programming Useful Life Prediction (PULP) project. We will use Charles River's Figaro probabilistic programming language (PPL) to fuse physical models of critical fault modes and data-driven methods in a hybrid approach to accurately predict the RUL of critical flight systems. Figaro is an ideal solution because it eases construction of Probabilistic Relational Models (PRMs). PRMs can represent a wide range of complex, constantly changing domains that involve uncertainty and require flexibility. Figaro also contains a vast library of reasoning algorithms that can be applied to models, so users do not need deep mathematical expertise.