> Would you say that you'd have got an AI researcher position without a Ph.D.?
It's difficult to get any true researcher position without a PhD. It doesn't mean that PhD has to be in AI. Research involves a lot of reading and writing papers, which a PhD is supposedly training you how to do.
That said most places will say "equivalent practical experience" and it's entirely possible to be competent in AI/ML without a PhD.
I did a PhD in space science, I now do machine learning in ecology and spent the summer working on machine learning for disaster management. The interesting jobs (to me) are where domains cross, and it's also (hint hint) much easier to get a job doing AI for X than it is doing "fundamental AI". In any case, you're often doing stuff that nobody has done before anyway, but you don't need to spend your life hunting for the new ResNet.
> Also, why is NEURIPS or ICML papers is not a hiring guarantee?
What the OP probably might be implying is that everyone has a publication in NeurIPS nowadays.
I think it goes deeper than that though, publishing in machine learning is broken. Having 10k people at one conference is not an efficient way to distribute research. You have to submit a full paper in November for a conference next Summer - pretty much only computer science does this madness.
What's interesting is how unique this attitude is. In astronomy, for example, conferences are a fun place to catch up with folks in your niche. There might be a few hundred people and probably it'll be single-track. We publish whatever journal is the most relevant and they're generally all considered equivalent. Nobody cares if you publish in ApJ vs A&A vs MNRAS, if your research is good.
There are also concerns that the quality of these venues is decreasing because the pressure to publish in them is so high.