Computer science is key to all of this, understanding how to write performance code isn't just in the programming skill, but it's also in understanding the complexity theory, graph theory and computer architecture.
When going for a job at these places, (you did specify 'ML Engineer', this is different from a researcher), they will grill you to make sure you understand how to use a computer. They want you to know it inside out, not just having done some tutorials, but actually able to debug deep complex issues and performance tune.
Getting familiarity with modifying the linux kernel is much more important than having done some silly competitions/tutorials. You have to remove the marketing material from reality.
It's not about being able to "write something in C", it's about understanding the conception of every bit of code, and what is happening underneath, to engineer a quality solution.
I did mention a course on algorithms and data structures as a requirement, that's where you learn about complexity, graphs, and other things.
I'm guessing you don't know what Kaggle is. It's not a silly competition. It's a place where you get exposed to real-world problems, using real-world data. This is a good alternative to an actual internship as a ML/Data engineer.
You expect ML engineers to know how modify the Linux kernel? Do you also want them to know how to design a superscalar processor in Verilog? How about simulating circuits with SPICE? I don't think so.
Kaggle is just a hobbyist competition. How many engineers do you think have used Kaggle to gain their employment? I get that it's hard to understand from the ivory tower of academia what is actually required in the real world.
You seem to keep missing the point, an engineer should be comfortable with working on the low level, understanding performance tuning and they should have a solid understanding of architecture on the low level. Experimenting with the linux kernel is one way to get an intuitive feel for how things work, not a requirement.
You also seem to keep misunderstanding the domain space, do you even know what an engineer does? What do you think they're doing all day? They're writing high performance code, not solving stupid riddles. They will almost never write anything MATLAB, they engineer things to specifications.
I hope your university has an internship, after you complete it maybe you'll understand then.
Because knowing how to implement something in C does not mean you know how to implement it in MATLAB, and the best way to learn to do both is to implement the same thing using both languages.
How many engineers do you think have used Kaggle to gain their employment?
I know two people personally who were asked, and bragged about, their Kaggle experience during ML engineer job interviews. Moreover, several of ML positions I was interested in had Kaggle experience mentioned as a desired qualification in the job description.
an engineer should be comfortable with working on the low level, understanding performance tuning and they should have a solid understanding of architecture on the low level
If you reread my comments, you will see that this was kind of my point (and a lot of people here disagreed with me on this). However, learning the details of the Linux kernel is not the best way to learn about computing, and is definitely not the best way to learn the skills needed to do machine learning.
do you even know what an engineer does?
Before enrolling in a PhD program, I worked as an engineer and an engineering manager for 12 years. Since I went back to school, I did two ML related internships, and I occasionally work on freelance ML projects for local companies. What are your credentials, relevant to this discussion?
I first learned about neural networks from deeplearning.net tutorials. They use Theano, so when I got a convolutional network running, I was left wondering about two critical pieces: backpropagation, and the actual convolution operation. Theano completely hides them from a user. So I decided to implement a simple convnet using only Python and Numpy. That was when I realized that I had no idea how the backprop works for convnets. I'm guessing most people who only used Theano or similar framework to run convnets don't know how backprop works there.
Next, I realized that the code I wrote was too slow for any practical purposes, and I decided to rewrite it in pure C. Well, actually I gave up half way and used Armadillo package (Numpy analog for C/C++), but the end result was my convnet became 5 times faster. It was still too slow, and I noticed that my code is using only one core out of 4 cores available on my computer, so I decided to parallelize it, using Cilk+. It was actually easier than I expected, and soon I got almost linear speedup using all 4 cores.
The final exercise was to implement it with CUDA, and I got another factor of 5 speedup running it on my GPU.
The benefit of this experience is enormous - I learned a lot about convnets, I learned a lot about tools and libraries useful for ML, and I learned a lot about making my code faster.
p.s. I don't quite understand what you meant about Kaggle and CS.
p.p.s. And yes, I do still have a lot to learn, but I wish someone gave me this advice when I was starting out.