  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
$0.08\,h/\mathrm{Mpc} \leq \Lambda \leq 0.20\,h/\mathrm{Mpc}$. 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.
