We present a new Julia library, TDAOpt.jl, which provides a unified framework for automatic differentiation and gradient-based optimization of statistical and topological losses using persistent homology. TDAOpt.jl is designed to be efficient and easy to use as well as highly flexible and modular. This allows users to easily incorporate topological regularization into machine learning models in order to optimize shapes, encode domain-specific knowledge, and improve model interpretability