I think of ML eng as more infrastructure and scalability. Possibly doing tasks like converting lab models into models that can be run at production scale. There is a blurry line between the two because it makes sense for some tasks to have shared ownership - just like you tend to have with people reaching across the stack to get something done in front-end vs. back-end web roles. As with anything, as you get more experience you get more comfortable jumping around and maintaining a larger set of concerns