If you look at model training jobs a lot of the work at this point is creating RL gyms (normal programming work), but most people still think the work is all neural architecture research. Doing the former is fine but won't teach you much about how to build LLMs, whatever that means now. Doing the latter is a very hard market to get into: not many jobs and requirements are often like, "you must have published at one of the following conferences". Prior experience is assumed. Most of them seem to treat Google as ML university and source of new recruits. It's understandable given the cost of training runs.
I'm not sure why it's like this. If you look at the real world, you have stuff like ggml, which is about as hardcore as it gets in the LLM space, and it was made buy just a guy. Same for this like ComfyUI
If you get enough academics in a place, they tend to close rank, and not let anyone in without the same credentials. Data science used to be like this, they were constantly on about how you need a Math Phd to even apply, yet when I met these guys IRL, most of them were just running Python math libraries.
These previous examples show that if you understand at least a part of the problem space, you can 100% contribute without academic credentials.
The question is more one of opportunity cost. At 17 you need to start finding your way in the world. It's best to learn skills lots of people need.