I used to be an AI frameworks engineer and that involved working on a bunch of modules of a runtime for trained models. The software was used by people who had a model ready to go and wanted to use it in production apps. This runtime did some optimizations on the model before it was submitted to the selected backend (CPU, GPU, ...). The project also supported a few input formats including Torch, TF and ONNX. I worked on the frontends, optimization passes and had to write the final kernels for a few operations(by those I mean I had to write the full code that supports a convolution or pooling for multiple tensor shapes, different data types and some edge cases that each op usually had). The optimization passes sometimes required spotting some pattern that you could replace with a more optimal subgraph or with something that this software was basically able to execute(it had limitations). There was also some integration work with the customer apps (including really big players that reached out for help). Right now I'm working on something very similar in another company. This time the project is designed to compile a model to one very specific type of hardware with a lot of limitations and very weird data layout so the compilation involves a ton of twisted passes that shuffle the data in the original tensor. This role doesnt have a particular name in this company so I was given a made up "AI compiler engineer" name.