Exactly what I had in mind. ML has its place in bioinformatics (and is being applied to a huge amount of sub-problems there), but it almost always is an incremental improvement for that niche rather than transforming the whole landscape.
I'm talking precisely about what this guy worked on - https://news.ycombinator.com/item?id=23540564
ML provides predictive capabilities over large fugly datasets. CRISPR gives us the ability to test those predictions. These are both essentially commodity technologies at this point and not everyone is going to take a disciplined approach to experimentation.
That being said, I can see some applications to using ML to analyze the results of CRISPR/Cas9 screens or better predict functional editing sites - but that's not exactly exclusive to Cas9.