That's basically how engineering works if you're doing anything at all novel: you will have some theory which informs your design, then you build it, then you test it and basically need to do science to figure out how it's perfoming, and most likely, why it's not working properly, and then iterate. I do engineering, but doing science has been a core part of almost every project I've worked on. (heck, even debugging code is basically science). There's just different degrees in different projects as to how much you understand about how the system you're designing actually works, and ML is an area where there's an unusual ratio of visibility (you can see all of the weights and calculations in the network precisely) to understanding (i.e. there's relatively little in terms of mathematical theory that precisely describe how a model trains and operates, just a bunch of approximations which can be somewhat justified, which is where a lot of engineering work sits)