This is not with named array dimensions, but with only JaggedArrays, masked selection is possible:

jarr_2d = aw.fromiter([[np.array([0, 1, 2]), np.array([3, 4, 5])],
                       [np.array([6, 7]), np.array([8, 9])]])
# <JaggedArray [[[0 1 2] [3 4 5]] [[6 7] [8 9]]] at 0x7fc9c7c4e750>

jarr_2d_mask = aw.fromiter([[np.array([False, True, False]), np.array([True, True, False])],
                            [np.array([True, True]), np.array([True, False])]])
# <JaggedArray [[[False True False] [True True False]] [[True True] [True False]]] at 0x7fc9c7c1e590>


jarr_2d[jarr_2d_mask]
# <JaggedArray [[[1] [3 4]] [[6 7] [8]]] at 0x7fc9c7c5b690>

Not sure if this code is efficient? Especially compared to fancy indexing with only Numpy arrays?

awkward.fromiter is the one function that was allowed to be written in Python for loops, and hence it is designated to be slow. The function you want for turning regular NumPy arrays into JaggedArrays that happen to have uniform counts is JaggedArray.fromregular. That ought to be considerably faster.

Meanwhile, your original issue is an example of an inconsistency in Awkward 0.x. In Awkward 1.x, the behavior of Awkward Arrays that happen to be regular and NumPy arrays with the same logical meaning are identical. You might want to consider awkward1.from_awkward0 in the awkward1 library to try it out. (It's a separate library because the interface is a little different and I don't want to break anyone's analysis!)