Hierarchical Bayesian inference is a powerful framework for analyzing structured data common in complex experimental settings, like multi-subject decision-making research. Probabilistic programming languages (PPLs) such as Pyro provide excellent tools for defining and inferring these hierarchical models, leveraging features like plate notation for concisely representing repeated structures and managing dependencies. However, a significant challenge arises when the underlying scientific model is a complex simulator with an intractable likelihood function, rendering standard PPL-based inference inapplicable. While Simulation-Based Inference (SBI) techniques can handle such simulators by learning likelihood (or posterior) approximations from simulations, they often lack native support for easily specifying and inferring complex hierarchical dependencies. This talk introduces a novel approach that bridges the SBI package sbi and pyro, enabling effective simulation-based hierarchical Bayesian inference. We demonstrate how likelihood approximations learned via sbi can be seamlessly integrated as custom components within pyro models. This synergistic approach combines the strengths of both methodologies: SBI's ability to perform inference on intractable simulators and Pyro's expressive power and efficiency in handling complex hierarchical structures. We will illustrate the potential and practicality of this integrated methodology using a key example from cognitive science: fitting a hierarchical drift-diffusion model (DDM) to choice data. The focus will be on how this combined "Pyro meets SBI" approach successfully allows for Bayesian inference over the parameters of the hierarchical model, effectively combining the information across multiple simulated subjects while handling the intractable DDM likelihood. This integration significantly expands the scope of rigorous Bayesian inference, opening new possibilities for analyzing complex, simulation-based models across various scientific disciplines. We will also briefly highlight how recent developments in the sbi package facilitate this powerful workflow, making advanced hierarchical modeling accessible for simulator-based research.