There are several branches, but all of them typically run at a scale where performance costs them money. People with large server deployments (any "hyperscale" company) hire these people to save millions of dollars a year. People who do client performance attract and retain users. Those who do trading bank on being the fastest. And many other areas; these are just the big ones.
Interesting. Does it need a PHD or a specific Master? Because I feel no one needs that kind of performance at home so the only place to train is academy or industry while industry usually does not train a junior on that topic.
This type of optimization isn't something you typically study as an academic. Everyone I know that does this kind of work (these jobs definitely exist) was self-taught by experimenting in their own time because they had a passion for performance optimization. It is a rewarding specialization in that there is a quasi-objective measure of incremental progress.
Much of this optimization work requires specific knowledge of the details of the operating environment (e.g. Linux) and silicon microarchitecture (e.g. AVX-512), which can be poorly documented. You have to be comfortable doing experiments and digging into system arcana to surface properties of the system that you can't trivially google. While algorithm selection is important, doing that well is table stakes and part of the role is knowing when and why an "optimal" algorithm is worse than e.g. selectively applied brute-force.
Computational efficiency and throughput is worth a lot of money at scale but most of it is designed and implemented at the scale of a single machine. Every software engineer owns the tools required to become proficient at this. It is genuinely a rare skill even among systems programmers and is a good way to separate yourself from the crowd.
I just became an AVX2 programmer for fun. You can too, if you go to the highload.fun group chat then I or someone else will suggest blazing fast ideas for every problem that happen to be about half as fast as our own approaches.
