[madenine]

"Machine Learning Engineer" is a title we're going to see more and more of (and we're already seeing a lot).

Its one thing to know the math and theory to design, train, and tune the algorithm your company needs. But implementing it into production, at scale? That's not the same person.

Ideally, you have Person/Team A, who designs but knows enough about implementation to keep that in mind during their process, and Person/Team B who implements it into the software but knows enough about the design to make it work.

[ska]

Truly ideally you have someone/team who actually can do both properly. However, very few people can. And if you have one, you may not be able to justify their time on all aspects.

So the compromise is usually as you describe, but you bear the cost of translation issues no matter how you do this. It's worth remembering that it is a compromise.

I think systems like tensorflow are implicitly a recognition of this, allowing lower impedance between the groups.