The Atmospheric Machine Learning Emulation Challenge (AMLEC) invites researchers, data scientists, and practitioners in the fields of remote sensing, climate science, and artificial intelligence to contribute solutions to a key computational challenge in atmospheric modelling. Organised by Jorge Vicent Servera, Gustau Camps Valls, Cesar Aybar, and Julio Contreras from the Image and Signal Processing Group (ISP) at the University of Valencia, AMLEC focuses on advancing surrogate modelling and physics-informed AI in the context of Radiative Transfer Models (RTMs).