In my experience it largely depends on the field within Software Engineering, and where it blurs into subfields of Applied Math / Engineering. Machine Learning

In my experience it largely depends on the field within Software Engineering, and where it blurs into subfields of Applied Math / Engineering. Machine Learning needs some things (mainly "easy" things like matrix multiplication and convolution with the occasional truncated SVD), Modeling of Physical Systems needs other things (PDE solvers), Computer graphics different stuff (mainly focusing on small rotation/transformation matrices applied to many many points), Nonlinear Optimization yet another subset (solution of large sparse systems), and that's before you get to signal processing and statistics. The main commonality is once you have the basic terminology down and understand that if you're explicitly inverting a matrix you're probably doing it wrong, you should just use the existing highly tuned libraries for your use area (at least until you decide it would be cool to try and beat them). Once you need to go beyond that you're more into the realms of matrix analysis and structure exploitation, and firmly have at least one foot in the math camp.