How do you get hired as ML engineer? What are the things to learn. How do you sell yourself by just learning all new frameworks and all on ML but no actual experience on ML.
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As a researcher/staff scientist, I expect ML engineers to take the inference portion of my sloppy Python code and reduce the latency of queries from 1s to below 100ms (we serve models to millions of users per second, so usually the target is even lower than that). This might entail refactoring my code, or writing some new stuff in a different language.
Ideal candidate would have in depth knowledge of a low level language (C++ or Java), Python, and runtime optimization. Knowing some ML architectures (at the very least; know how the data flows from input to output for conv/recurrent/feed forward neural networks, and approximate nearest neighbors), and an ML framework like tensorflow is a huge bonus.
Learn the basic ‘standard’ state of the art models at a high level to demonstrate curiosity and the ability to learn (reading papers is a necessity in this field, but they tend to be pretty light on hard math). In CV you should know convolutions, resnet and maybe some object detection models like faster rcnn. NLP is maybe language models, transformer and perhaps some RNN I’m not familiar with.
Learn one of the major frameworks and actually use it. Optional because it’s time consuming, but implementing a real model (i.e. not a regression) will probably help give you something to talk about in interviews.
This will tell you if you have the math skills or you need to study
https://davidrosenberg.github.io/mlcourse/Notes/prereq-questions/math-questions.pdf