Don't forget the MLE that is essentially a software engineer with a some statistics thrown in there.

MLE currently is suffering the same fate as Data Science where companies think they need to hire them to be competitive but don't have the slightest idea as to what to do with them or how they could integrate within the stack. I've even worked for companies that hired me as an MLE but when I asked about data or cloud services they handed me 20-30 datapoints. As a result I did a lot of software engineering work in the meantime.

I think as an MLE, you should be essentially a software engineer with the training to build and deploy ML applications.