  Large-scale detectors consisting of a liquid scintillator target surrounded
by an array of photo-multiplier tubes (PMTs) are widely used in the modern
neutrino experiments: Borexino, KamLAND, Daya Bay, Double Chooz, RENO, and the
upcoming JUNO with its satellite detector TAO. Such apparatuses are able to
measure neutrino energy which can be derived from the amount of light and its
spatial and temporal distribution over PMT channels. However, achieving a fine
energy resolution in large-scale detectors is challenging. In this work, we
present machine learning methods for energy reconstruction in the JUNO
detector, the most advanced of its type. We focus on positron events in the
energy range of 0-10 MeV which corresponds to the main signal in JUNO --
neutrinos originated from nuclear reactor cores and detected via the inverse
beta decay channel. We consider the following models: Boosted Decision Trees
and Fully Connected Deep Neural Network, trained on aggregated features,
calculated using the information collected by PMTs. We describe the details of
our feature engineering procedure and show that machine learning models can
provide the energy resolution $\sigma = 3\%$ at 1 MeV using subsets of
engineered features. The dataset for model training and testing is generated by
the Monte Carlo method with the official JUNO software.
