n_jobs = -1 equivalent in keras
Here you do a single fit() of the model whose name tells for itself - Sequential. 

Unless you are doing cross-validation or some kind of distributed learning with multiple models, there is no benefit of running several fits in parallel.

However, you can have significant speed-up on iteration level, depending on how Keras backend is configured to work with your hardware.

Assuming you are using Keras with the tensorflow backend (default):


If you are running on CPU, tensorflow should already pick up all available cores by default (if not, it might help checking How to run Keras on multiple cores?); 
If you are running on GPU:

(1) make sure Keras backend can see your GPU

from keras import backend
print(backend.tensorflow_backend._get_available_gpus())


If your GPU is not showing up here, some configuration might be necessary
on your system (it deserves another topic);

(2) take a look at the CuDNNLSTM - it is the Keras LSTM implementation
optimized for GPU.