BERT produces a 768-dimension vector for each token, processed to take into account a small amount of information about each of the other tokens in the input text.   Then it can be made to combine all of those vectors (for a single input text) into a single 768-dimension vector that can be considered as a representation of the whole input text.
I believe it should be possible for you to access both the token-vectors and the whole-text-vector, without having to delve into the model code.
With a bit more trouble, you could create your own vector that is a combination of only the tokens from the job description.   The tricky bit is deciding how to combine the vectors.  Some researchers suggest using the vectors from each of the last four layers.
What are you planning to do with the embeddings once BERT has created them?   Do you want your “job description” embedding to include a small amount of context information from your date/title/name?  If not, you would need to put them through a separate BERT.
Are you planning to use BERT just to produce embeddings, or are you planning to fine-tune BERT to your task?
Note that BERT will only accept a maximum of 512 tokens per text.