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Recently, hybrid bias expansions have emerged as a powerful approach tomodelling the way in which galaxies are distributed in the Universe. Similarly,field-level emulators have recently become possible thanks to advances inmachine learning and $N$-body simulations. In this paper we explore whetherboth techniques can be combined to provide a field-level model for theclustering of galaxies in real and redshift space. Specifically, here we willdemonstrate that field-level emulators are able to accurately predict all theoperators of a $2^{\rm nd}$-order hybrid bias expansion. The precision achievedin real and redshift space is similar to that obtained for the nonlinear matterpower spectrum. This translates to roughly 1-2\% precision for the powerspectrum of a BOSS and a Euclid-like galaxy sample up to $k\sim 0.6 h^{-1}$Mpc.Remarkably, this combined approach also delivers precise predictions forfield-level galaxy statistics. Despite all these promising results, we detectseveral areas where further improvements are required. Therefore, this workserves as a road-map for the developments required for a more completeexploitation of upcoming large-scale structure surveys.

Hybrid-bias and displacement emulators for field-level modelling of galaxy clustering in real and redshift space