Yeah, I'd describe myself as pretty driven. The tech I work with is very cool, we do a little bit of everything but for the most part I run a GPU cluster that students in fields related to machine learning use to do their homework on. My undergrad was in ML before this cluster existed so I remember trying to train neural nets on my laptop with a 2nd gen i7 and no GPU, every time I ran my code I'd have to go do something else for eight hours while it chugged. So not only do I get to work on something that I know in my bones is improving lives (and saving sleep!) to at least some degree, I get to play with cool toys like Kubernetes and fancy ZFS arrays and all that much earlier in my career than most people do.

So I have the intrinsic motivation to do the work now, because I want this resource to be the best it can be for the students who use it. But also I have the career and financial calculus that the more I learn in this environment, the better I'm set up to have an impact on it in the long term if I stay (which I currently plan to), or the better set up I am to find something equally fulfilling if I get laid off.

It's already paying dividends too, front-loading a lot of the foundational learning and hustle means that even though I do higher level stuff it takes less time and beating my head against the wall to do it. I think I'm actually a bit slower command-for-command than I was a year or two ago, but it's because I'm doing stuff like setting up container environments for legacy software loads so we can run them on current operating systems, rather than just muddling my way through patching a bunch of VMs.