Engineering video
1:06 · 25 August 2025
Building AI products requires extensive software engineering work like APIs, deployment, SDKs, UIs, and infrastructure management, whereas machine learning modeling is typically handled by a small group of researchers.
Hear it at 0:00 →Data scientists and machine learning engineers experienced higher unemployment rates in 2023 and 2024 than traditional developers, who remain in high demand because they can add value to small companies using diverse tools.
Hear it at 0:36 →Most machine learning products, they need a lot more software engineers to make them reality than they need machine learning engineers. You have a lot of distribution, you need to build APIs, you need to deploy, you need to manage infrastructure. You need to build SDKs, you need to build UIs, you need to manage traffic. All those things are done by developers, and that's why you have so many developers working nowadays where computer science in software engineering, it's the biggest engineering discipline in the US because business runs on software.
And you need all these people to make everything reality, to people building the model, you'll have those people, but there's usually a a handful of researchers that are doing that. And we actually seen in 2023 and 2024 that the unemployment for data scientists and machine learning engineers was even higher than for traditional developers. Because guess what, a traditional developer, a Java dev, a JavaScript developer, they can go work for a very small company and still help them by using, you know, low code and setting up their website.
You can do a lot of things there and add value, whereas someone that knows machine learning, they can only work for a handful of companies because they're very specialized.
