  Recently, hybrid bias expansions have emerged as a powerful approach to
modelling the way in which galaxies are distributed in the Universe. Similarly,
field-level emulators have recently become possible thanks to advances in
machine learning and $N$-body simulations. In this paper we explore whether
both techniques can be combined to provide a field-level model for the
clustering of galaxies in real and redshift space. Specifically, here we will
demonstrate that field-level emulators are able to accurately predict all the
operators of a $2^{\rm nd}$-order hybrid bias expansion. The precision achieved
in real and redshift space is similar to that obtained for the nonlinear matter
power spectrum. This translates to roughly 1-2\% precision for the power
spectrum 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 for
field-level galaxy statistics. Despite all these promising results, we detect
several areas where further improvements are required. Therefore, this work
serves as a road-map for the developments required for a more complete
exploitation of upcoming large-scale structure surveys.
