I think it depends on the job. Maybe a web-developer has lesser gain from extensive knowledge in ml. But I agree that every computer scientist (whether he works as an software engineer or not) should have some knowledge of ml, there are many things in the curriculum that are not as important as ml.

As a snarky remark: Maybe i am not yet qualified enough for real criticism as an cs-student, but i don't like it such sharp destinations between engineering and theory. All the "trial an error" in ml can be a useful guide to solving the theory. Also i guess the work of Jeff Dean is quite often more theoretical as the work of an average engineer. While i feel that if we have not developed a theory behind such tools, we have not really understood them, no one knows how komplex these things really are. I think/feel this makes ml-related engineering harder than software projects with a well understood theory

I just hope there are enough computer-scientists/mathmaticians at universities (or google ;) ) sharply looking on all the progess made in ml from the engineering side and asking themselves "what does that really mean?", because thats a hell of an interesting problem.

I may be wrong, my lecture on ml is next semester ;)