Physics analyses at the CERN experiments rely on detector hits being interpreted or reconstructed as particle candidates. The data reconstruction systems are built on decades of physics and detector knowledge and must operate reliably on petabytes of data in diverse computing centers spread around the world. In the recent years, machine learning (ML) is playing an increasingly important role at the LHC experiments for reconstructing and interpreting the data, from calibrating the detector readouts to the final interpretation for complex signal processes. We will discuss the various aspects of ML at the LHC experiments, focusing on data reconstruction and particle identification approaches using modern machine learning methods such as graph neural networks. We will bring a concrete detailed example from machine learned particle flow (MLPF), an R&D effort to develop a fully optimizable particle flow reconstruction across detector subsystems in CMS.