I've seen both in bioinformatics, because the field is so wide now. 1) Bioinformatics as tool-building, algorithm-dev: you're right, you don't need to know muc

I've seen both in bioinformatics, because the field is so wide now. 1) Bioinformatics as tool-building, algorithm-dev: you're right, you don't need to know much biology there if the problem is defined well. 2) Bioinformatics as a tool to answer biological questions: here I've seen ML-background people really struggle, either developing stuff that's not useful or reinventing-the-wheel-but-now-it's-deep-learning. I've seen ML people present their fancy plant disease image detector which turned out to be pretty good at spotting 'yellow' - very good at training accuracy and benchmarks, does not add anything to what people in the field are doing.