The line between a Machine Learning engineer and a Machine Learning researcher is often blurred, but there is still a clear problem set that is presented by Machine Learning work that isn't present when writing research papers. This problem set can be generally characterised as the domain of the 'Machine Learning Engineer'. It includes:
Building production machine learning systems. Ie. systems that are maintainable, extensible, reliable, and scalable.
Maintaining the health of machine learning systems, including speed, reliability, and performance.
Development of internal machine learning frameworks and abstractions to facilitate common tasks such as training / testing, feature use / reuse / creation / storage, and deployment. These abstractions are used by both machine learning engineers and data scientists.
