Cosmology inference of galaxy clustering at the field level with the EFT
likelihood in principle allows for extracting all non-Gaussian information from
quasi-linear scales, while robustly marginalizing over any astrophysical
uncertainties. A pipeline in this spirit is implemented in the
\texttt{LEFTfield} code, which we extend in this work to describe the
clustering of galaxies in redshift space. Our main additions are: the
computation of the velocity field in the LPT gravity model, the fully nonlinear
displacement of the evolved, biased density field to redshift space, and a
systematic expansion of velocity bias. We test the resulting analysis pipeline
by applying it to synthetic data sets with a known ground truth at increasing
complexity: mock data generated from the perturbative forward model itself,
sub-sampled matter particles, and dark matter halos in N-body simulations. By
fixing the initial-time density contrast to the ground truth, while varying the
growth rate f, bias coefficients and noise amplitudes, we perform a stringent
set of checks. These show that indeed a systematic higher-order expansion of
the velocity bias is required to infer a growth rate consistent with the ground
truth within errors. Applied to dark matter halos, our analysis yields unbiased
constraints on f at the level of a few percent for a variety of halo masses
at redshifts z=0,0.5,1 and for a broad range of cutoff scales
0.08h/Mpcβ€Ξβ€0.20h/Mpc. Importantly,
deviations between true and inferred growth rate exhibit the scaling with halo
mass, redshift and cutoff that one expects based on the EFT of Large Scale
Structure. Further, we obtain a robust detection of velocity bias through its
effect on the redshift-space density field and are able to disentangle it from
higher-derivative bias contributions