I use CMA-ES (a type of evolutionary algorithm) for training neural networks for finance stuff. The neural networks involved are not superhuge so it works out (IIRC the number of parameters is around ~500-1000).

The fitness function is pretty complicated and written in Rust and I put a lot of effort to making it fast because these algorithms need to evaluate it many many times. I feel using evolutionary algorithms makes coding simpler because you do not need to care that whatever you are writing is differentiable or that some backprop/gradient descent library needs to be able to "see" inside your function.

I do think my use case is a bit more niche. I live in hope that some breakthrough happened that made evolutionary algorithms practically usable for large neural networks.