Beyond what makes a good model, IME at a FAANG building an ML product, the bulk of the work in practice tends to be general software engineering. There need to be a sufficient number of people who understand the actual ML pieces under the hood, but even when you're making changes to the models, the bulk of the actual work is not complex ideation, but implementing the ideas in software, and this implementation is usually something you can learn in a few months.