I think there are several reasons this division of labor is very minimal, at least in some places.
You need way more of the ML engineering / fixing stuff skill than ML research. Like, vastly more. There are still a very small handful of people who specialize full time in thinking about research, but they are very few and often very senior. This is partly an artifact of modern ML putting way more emphasis on scale than academia.
Communicating things between people is hard. It’s actually really hard to convey all the context needed to do a task. If someone is good enough to just be told what to do without too much hassle, they’re likely good enough to mostly figure out what to work on themselves.
Convincing people to be excited about your idea is even harder. Everyone has their own pet idea, and you are the first engineer on any idea you have. If you’re not a good engineer, you have a bit of a catch-22: you need promising results to get good engineers excited, but you need engineers to get results. I’ve heard of even very senior researchers finding it hard to get people to work on their ideas, so they just do it themselves.
I think there are several reasons this division of labor is very minimal, at least in some places.
You need way more of the ML engineering / fixing stuff skill than ML research. Like, vastly more. There are still a very small handful of people who specialize full time in thinking about research, but they are very few and often very senior. This is partly an artifact of modern ML putting way more emphasis on scale than academia.
Communicating things between people is hard. It’s actually really hard to convey all the context needed to do a task. If someone is good enough to just be told what to do without too much hassle, they’re likely good enough to mostly figure out what to work on themselves.
Convincing people to be excited about your idea is even harder. Everyone has their own pet idea, and you are the first engineer on any idea you have. If you’re not a good engineer, you have a bit of a catch-22: you need promising results to get good engineers excited, but you need engineers to get results. I’ve heard of even very senior researchers finding it hard to get people to work on their ideas, so they just do it themselves.
This is encouraging to hear as someone with relatively little ML research skill in comparison to experience with engineering/fixing stuff.