[emilwallner]

This is something I've also struggled with. I find it hard to read deep learning papers because I need to translate each math notation, thus struggling to get the bigger picture. I'm fond of the bottom-up approach, e.g. I started by mastering C and wrote my own libraries. But for deep learning I lean towards the opposite, starting with high-level libraries. When I want to understand the theory I search for simple python code that I can implement from scratch. This way I can understand the logic, without having to understand all the math behind it. I've mostly focused on doing Kaggle type of problems and used MOOCs when I get stuck. I've had little interest from larger companies, but I've managed to get a few offers from startups. Startups often have a couple of people with PhD-level knowledge but are also looking programmers that can code the models.

[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.

[EternalData]

There's a difference between people who can implement models and those that can create them -- startups could use people who do the former, and many don't actually need the latter.