Keras: Masking and Flattening

I'm having difficulty building a straightforward model that deals with masked input values. My training data consists of variable-length lists of GPS traces, i.e. lists where each element contains Latitude and Longitude.

There are 70 training examples

Since they have variable lengths I am padding them with zeros, with the aim of then telling Keras to ignore these zero-values.

train_data = keras.preprocessing.sequence.pad_sequences(train_data, maxlen=max_sequence_len, dtype='float32', 
                                           padding='pre', truncating='pre', value=0)

I then build a very basic model like so

model = Sequential()
model.add(Dense(16, activation='relu',input_shape=(max_sequence_len, 2)))
model.add(Flatten())
model.add(Dense(2, activation='sigmoid'))

After some previous trial and error I realised that I need the Flatten layer or fitting the model would throw the error

ValueError: Error when checking target: expected dense_87 to have 3 dimensions, but got array with shape (70, 2)

By including this Flatten layer, however, I can not use a Masking layer (to ignore the padded zeros) or Keras throws this error

TypeError: Layer flatten_31 does not support masking, but was passed an input_mask: Tensor("masking_9/Any_1:0", shape=(?, 48278), dtype=bool)

I have searched extensively, reading GitHub issues and plenty of Q/A here but I can't figure it out.

from Keras: Masking and Flattening