> Does doing NLP essentially mean having to learn ML at this point?
Yes. Everything in NLP nowadays involve ML. Most NLP problems have some structure (e.g., generating a sequence from another sequence like in Machine Translation, or predicting a tag for each word in a sequence like in part of speech tagging). Once you have good ML fundamentals it's not that difficult to get into NLP.
Also, even though different tasks in NLP share structure and characteristics, it's a large field with different areas of expertise. You don't need to know everything. Focus on the problems that interest you first.
Some really nice projects do NLP without using ML at all, for instance Duckling [1] (a library made by facebook to find entities in a text) works a 100% with parsing rules, and is surprisingly efficient.
I agree with your point though, most of the time there is ML at some point in your pipeline so you can't really avoid learning it !