No one (well, few professionals at least) will reinvent the wheel when it comes to standard scientific computations and methods. Like numerical math, linear algebra, etc.

Looking through the problem sets in the link, the majority seems to be asking for just that.

If you're wondering whether or not someone knows how to transpose a matrix, or find the eigenvalues, let them do that on the whiteboard. No need to leetcode-ify such problems, because with 99.99% probability they'll provide you with solutions that are subpar compared to industry standard packages. There's more than time and space complexity when it comes to these problems.

EDIT: Also, you'll potentially lose a lot of high-quality candidates if you suddenly start to test people on methods they haven't worked with or seen in quite a while.

If you ask something like "please show us the equations for a support vector machine, and how you can compute a SVM" you could fail even world class ML scientists, if they haven't touched those for 10 years. Which is a very real possibility in the current ML scene.

I'd say that almost every ML interview I've had, or been part of, have been more big picture whiteboard interviews. Specific programming questions have ranked quite low on things to prioritize.