I believe no one would argue against applying good engineering principles to any project. But for the context of academia ML research, reproducibility is the #1 reason for being open-sourced in the first place. People really don't care if your logger or plots are beautiful or reusable; they just want your code to a) run with minimum yak shaving and b) have clear instructions of what things are — yet that's already hard enough for many repos I have came crossed :(

For researchers that are doing the development, yes, it'd be nice to have some reusable/expandable modules. Most people I know have the same utils.py as the base template for similar projects, well-maintained open-sourced projects often have nice modules that you can borrow, there are also many well-built experiment trackers that you can make use of. If you got more routine stuff, services like AWS SageMaker are out there. It is just people don't care much about them when it comes to (most) research sharing, they are just more dependencies to track. If you are in a more production-focused role that'd be a different story.