We extend the algorithm presented by Han, Jentzen and E to Navier-Stokes in high dimension, which is an initial boundary value problem. The equation is reformulated using backward stochastic differential equations and the gradient of the unknown solution is approximated by neural networks. Numerical examples show the accuracy of the algorithm, which is quite effective in high dimension.