  In this paper we present an extension to the $\texttt{matryoshka}$ suite of
neural-network-based emulators. The new editions have been developed to
accelerate EFTofLSS analyses of galaxy power spectrum multipoles in redshift
space. They are collectively referred to as the $\texttt{EFTEMU}$. We test the
$\texttt{EFTEMU}$ at the power spectrum level and achieve a prediction accuracy
of better than 1\% with BOSS-like bias parameters and counterterms on scales
$0.001\ h\ \mathrm{Mpc}^{-1} \leq k \leq 0.19\ h\ \mathrm{Mpc}^{-1}$. We also
run a series of mock full shape analyses to test the performance of the
$\texttt{EFTEMU}$ when carrying out parameter inference. Through these mock
analyses we verify that the $\texttt{EFTEMU}$ recovers the true cosmology
within $1\sigma$ at several redshifts ($z=[0.38,0.51,0.61]$), and with several
noise levels (the most stringent of which is Gaussian covariance associated
with a volume of $5000^3 \ \mathrm{Mpc}^3 \ h^{-3}$). We compare the mock
inference results from the $\texttt{EFTEMU}$ to those obtained with a fully
analytic EFTofLSS model and again find no significant bias, whilst speeding up
the inference by three orders of magnitude. The $\texttt{EFTEMU}$ is publicly
available as part of the $\texttt{matryoshka}$ $\texttt{Python}$ package.
