It's a question of risk aversity.

DeepMind is the safe option. It's prestigeous and a large company.

A startup is likely to fail. You should be prepared for that. In the unlikely case that the startup succeeds, I'd prefer the startup over DeepMind.

So it depends on how likely you think it is that the startup succeeds and how much you would "miss out" if it doesn't.

I see. In the tutorial, for each output, a 1-dimensional Dense layer with a sigmoid activation function is used, along with binary crossentropy as the loss function. You could exchange that by an n-dimensional Dense layer with softmax activation, along with categorical crossentropy. So the basic architecture can remain similar, you just have to adapt the outputs.

This looks like a multilabel or multi output classification to me. Exactly the same thing was done here: https://www.kaggle.com/code/cbrincoveanu/transfer-learning-and-multi-output-tutorial Hope this helps.

Kudos for working so hard on side projects to prepare your ML career!

I think that's the way to go. If possible, try to get some feedback from experienced ML engineers on code quality, methodology and used libraries. Make sure to at least learn the basics of all the libraries that your potential employer could require.

If you can demonstrate your skills (perhaps in a live coding test), you can make it! I have colleagues who worked in the humanities before learning programming and becoming ML engineers, so it's possible :)