In this study, we demonstrate an internationally distributed MAP orchestrated by FI-NALES as shown in Figure working on two independent optimisation tasks in parallel. Both optimisation tasks vary the composition of a battery electrolyte composed of ethylene carbonate (EC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF 6 ), but one targets the optimisation of the ionic conductivity, while the other aims to maximise the End Of Life (EOL) of coin cells. We showcase the hierarchical acceleration in complex device level MAPs, both by intelligent sampling of the design space (here the chemical space of the electrolyte) as well as accelerating individual slow and resource-intensive tasks (here EOL testing) with ML. We further demonstrate the use of complementary methods for determining the ionic conductivity enlarging the chemical space accessible to the MAP as a whole. Moreover, we show the multi-tasking capability of our MAP by running the two optimisation tasks in parallel on the same infrastructure. The setup was able to discover the correlation between the concentration of the salt and the ionic conductivity of the electrolyte and showed reasonable agreement between predicted and experimentally determined median EOL.