Multi-class text classification with LSTM in Keras
After I read the source code, I find out that keras.datasets.imdb.load_data doesn't actually load the plain text data and convert them into vector, it just loads the vector which has been converted before.

As for your problem, I assume you want to convert your job_description into vector. Maybe you can try sklearn.feature_extraction.text.CountVectorizer. 

I'm no expert on NLP, but I've encountered problems (e.g. Two Sigma Connect: Rental Listing Inquiries) which need such technique.

In the meanwhile, there are other word2vec/embedding techniques you may try. Here is a tutorial from keras which shows a detailed example on word embedding.

References

https://github.com/fchollet/keras/blob/master/keras/datasets/imdb.py

http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

https://www.kaggle.com/sudalairajkumar/two-sigma-connect-rental-listing-inquiries/xgb-starter-in-python