Your statement that researchers build their network from the ground-up using C++ or some other low level library couldn't be further from the truth.
You could take a look at this analysis showing the popularity of these two frameworks in the top ML conferences. The following Figure is taken from there.
In CVPR-2020, for example, TensorFlow and pytorch combined for over 500 papers! Furthermore, because the two most active research entities (Google and Facebook) are backing these two frameworks, they are used in some of the most impactful research studies.
I want to give some reasons that support the popularity of these frameworks, but first I'm going to rephrase your question a bit:
Why use TensorFlow/Pytorch in python rather than build your model on your own using C++?
Note: The reason I rephrased the question is because TensorFlow and PyTorch both have a C++ APIs.
Why are these frameworks so popular in contrast to lower-level programming languages?
Some reasons are the following
Rapid prototyping. Languages link C++, have bloated syntaxes, require low-level operations (e.g. memory management) and cannot be run interactively. This means it takes someone much less time to create and test a model in python than it does in C++.
No need to re-invent the wheel. Some operations are common in most networks (e.g. backpropagation), why re-implement them? Other functionalities are hard to implement on your own (e.g. parallel processing, GPU computation). Do data scientists need to have such a strong technical background to research neural networks?
Open-source. They benefit from being opensource and can offer a great deal of tools at your disposal for building neural networks. You want to add batchnorm to your network? No worries, just import it and add it in a single line! Also, they offer the perfect opportunity for sharing pretrained models.
They are optimized. These frameworks are optimized to run as fast as possible on GPUs (if available) or CPUs. It would be virtually impossible for someone to write code that runs as fast on his own.