[apohn]

IME the core issue here is that there are far more SWE jobs than ML/DS jobs, which leads to a far greater variety of SWE jobs, which means people are better able to find SWEs jobs where they are able to find a job that matches their job environment. If you want to coast, there are SWE jobs for that. If you want to work 12 hour days on the bleeding edge, there are jobs for that as well.

>A lot of the time you could train model prepare pipeline and test enviroment for 3-6 months before you get good enough result to push it into production. And it can get extremely stressful rly fast if you care about that

Some of the the major job challenges in ML/DS are that the field is new, the number of jobs are far fewer than SWEs, the teams are small, and the people who can exert control of you have wildly unrealistic expectations of what it takes to build a useful model. So it's easier to land in a crappy job where stakeholders add to your stress because you are not "delivering" according to their definition of "I thought ML would solve all my business problems in 2 weeks."

I personally prefer ML/DS to what I see a lot of SWEs do. Yes, it can take months to get something working (or maybe it will fail after all that effort), but I'm also involved in the end to end of what I'm building and I get be part of defining the the "what", "why", and "how" and not just crank out yet another story on a Kanban board. What really helps me in my job is having a management chain that pushes back and educates stakeholders on why things take time and why that time is required to have a certain level of quality.

There are plenty of SWEs (on HN and elsewhere) who say they feel like factory workers. Somebody else makes a bunch of decisions and the SWEs simply churn out features and stories. So the quick wins turn into an endless flood of drudgery, just like how when you learn to fix a toilet you feel great, but if you fix 20 toilets a day, it probably becomes monotonous pretty quickly.