  We present an emulator that accurately predicts the power spectrum of
galaxies in redshift space as a function of cosmological parameters. Our
emulator is based on a 2nd-order Lagrangian bias expansion that is displaced to
Eulerian space using cosmological $N$-body simulations. Redshift space
distortions are then imprinted using the non-linear velocity field of simulated
particles and haloes. We build the emulator using a forward neural network
trained with the simulations of the BACCO project, which covers an
8-dimensional parameter space including massive neutrinos and dynamical dark
energy. We show that our emulator provides unbiased cosmological constraints
from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that
mimics the BOSS-CMASS sample down to nonlinear scales
($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly
extracting cosmological information from small scales using observations of the
large-scale structure of the Universe.
