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Confession: What makes me good at machine learning research makes my life miserable. And that is: I habitually stick with a problem until it's solved. The problem is that the problem can never truly be solved in machine learning. 1/
Engineering problems, in contrast, are generally finite. That's fun. The obsessive problem solving has a natural end: When the problem is solved. Two years of having an unsolved problem with this type of personality? That SUCKS. Now I know. 2/
I plan on writing more about this stuff later- about to wrap up research with a hard deadline. It's going to be a relief I think. Then what? Time to get a life again. 3/
Ordinary people have to tolerate a imperfections from time to time. ML practitioners tolerate more imperfections in a batch to make other people's life easier to tolerate.
For all its downsides, publishing at conferences with paper deadlines forces you to set "finite milestones" in your research. Each particular milestone is finished when the paper is submitted (or when it's eventually accepted, which is my preferred path).
I can definitely see the merits of that now.
I feel you. Also the ability to spot bugs in a code, without even having to launch a debugger, can complicate your life every now and then 
I tend to focus too much on what’s wrong in any situation
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