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Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between b -quark, c -quark, and light parton jets. These techniques are applied to a search for inclusive and decays using a LHCb dataset corresponding to an integrated luminosity of 1.6 fb − 1 . The observed (expected) 95% confidence level upper limits correspond to 6.6 (11.1) times the SM cross-section for the process, and 1003 (1834) times the SM cross-section for the process.