Around 2015, I found myself managing back end and machine learning engineers (not researchers) at the same time. Many of the back end engineers wanted to do more ML. Some of them did well when given a chance, but others wanted to revert to back end within a few months. At the same time, one of the ML leaders wanted to step away from ML and only do back end work to support ML.
As I studied these dynamics, something occurred to me... Different people need to see signs of success at different frequencies. Because of the nature of our product, measuring the performance of a new/updated model required the model to be live for at least a full calendar month. So, between initial work and final analysis, it was often a 2 month wait or more. For many back end tasks, you can build a quick prototype, run it to see if it works, and be on your way - the signals come all day long. The varying frequency needs of different people went a long way to determining which of them liked working on ML.
This is sort of a manager's version of feature engineering. ;-) The people on that team taught me a lot!
I saw the same thing and always wondered how you can manage it effectively.
I had a team of data engineers that wanted to do more data science, and 2 data scientists that both wanted to be data engineers(one of them argued that everyone wants to be DS and so it was too crowded, saying that they could make more money as a DE).
I also remember a specific instance where, one day, my friend ranted about how he needs to step away from pure front end and that it's a dead end career (he was quite good at it too!) and then the next day at lunch a colleague started complaining about how front end developers get all the credit and he's considering moving.
Sometimes it's a "grass is greener on the other side of the fence" thing or a FOMO thing. But sometimes different roles fit different people better.
I started my career doing mostly full stack work. I couldn't get away from the front end part quickly enough. I had intuition for simplifying UI flows, but none at all for aesthetics. As requests for aesthetic changes came in from our excellent designers, they felt completely arbitrary to me, even though they probably weren't. Most of my career was as a data engineer, data engineering manager, or leading an ML-heavy org. That space fit me so much better.
I loved having a few self-starting front-end devs in my orgs - they could take various tools we were creating for ourselves and make them quite a bit more useful. But it was also always a stepping stone as they typically wanted to work on the public facing part of the product.