Machine vision is actually a good place to be in this context. There is a lot of institutional knowledge on what good features are, and if you have enough data DNNs can do a lot of heavy lifting. If you arent doing DNNs, the decision on how you featurize the problem will probably play the most significant role.
Its some of the other areas of ML where its not known ahead of time what the good features would be that tooling for feature engineering would help a great deal. Especially when one doesn't have the luxury of throwing a DNN at it and be done with it. The situation has both good and bad parts, the good part is that there is a lot of space to be creative in the design of features.
