No, I think you misunderstood me. We engineer the model-training algorithm and then we USE the model. The model is a mess of random gobbledegook that happens to be the best optimizer for the training algorithm's reward function.

This is still science, in the same way it's still science when we breed cows to produce more milk without understanding the full bovine genome.

If something isn't right with the model we pour extra effort into our science - the training algorithm - and train a new one.

At no point does anything ever require peering into the slurry of random digits that make up the model itself, any more than using a computer requires understanding its DRAM training values computed at run time, or any more than pouring water requires understanding the laminar flow along the pitcher's surface (an unsolved math problem!).