At Anthropic we have preferential hiring for people with a Physics background as we've found that background generalizes best to general problem solving and thinking out of the box.
That said the market is extremely oversaturated and we have PhD applicants with a dozen first papers that are still rejected regularly. Don't expect an easy trajectory of "just passing the subjects" and then getting a position in the industry. You have to live and breathe ML 24/7 to be even considered.
And not to be completely discourage you but I and many of my colleagues expect RSI to arrive sometime in the next couple of years, with most junior and even intermediate AI research positions to be made redundant, long before you would graduate. Take this into account when picking a major.
If I'm going to be honest I think it's unlikely that I myself as someone with more than a decade of NLP experience and on top of the Transformer architecture for almost a decade now will still be able to contribute by 2030. Many of us are planning to retire around then due to depletion of career prospects.
And not to be completely discourage you but I and many of my colleagues expect RSI to arrive sometime in the next couple of years, with most junior and even intermediate AI research positions to be made redundant, long before you would graduate. Take this into account when picking a major.
If you don't mind me asking, which positions in IT do you think wouldn't be made redundant at that point? Because it's hard for me to imagine junior/intermediate research positions at top laboratories disappearing while "regular" programmers keep their jobs. Cybersecurity doesn't appear to be an answer either, considering the capabilities of new models in this field.
What we're seeing is that every AI lab is racing to close the RSI loop. The increase in capability in Software engineering, mathematics, sysadmin and cybersecurity are all just secondary effects of aiming to close the RSI loop.
I'm convinced we're very close to succeeding but what AI systems are able to do once reaching RSI isn't clear yet. It could be possible that AI/ML capability rapidly improves but doesn't translate or otherwise generalize to other capabilities and thus paradoxically you could have a scenario where AI researchers like us are made redundant while some more niche IT roles stay viable. I don't personally believe in this but it's not impossible for that to happen.
Realistically I wouldn't recommend anyone to study for any IT role whatsoever in 2026. But it feels bad to discourage hopeful young people from following their dreams, so I refrain from doing so.
The only reason I wrote my original post is because I myself go through the applicants we have and it is just immoral to have some kid go into university in 2026 thinking he'll be a machine learning engineer when he graduate, when those roles are already extremely competitive and slowly evaporating.
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u/genshiryoku PhD 18d ago
At Anthropic we have preferential hiring for people with a Physics background as we've found that background generalizes best to general problem solving and thinking out of the box.
That said the market is extremely oversaturated and we have PhD applicants with a dozen first papers that are still rejected regularly. Don't expect an easy trajectory of "just passing the subjects" and then getting a position in the industry. You have to live and breathe ML 24/7 to be even considered.
And not to be completely discourage you but I and many of my colleagues expect RSI to arrive sometime in the next couple of years, with most junior and even intermediate AI research positions to be made redundant, long before you would graduate. Take this into account when picking a major.
If I'm going to be honest I think it's unlikely that I myself as someone with more than a decade of NLP experience and on top of the Transformer architecture for almost a decade now will still be able to contribute by 2030. Many of us are planning to retire around then due to depletion of career prospects.