I know this might sound basic, but what’s the difference between a Machine Learning (ML) Engineer and a Data Scientist? Is an ML Engineer’s role solely focused on machine learning and deep learning models, while a Data Scientist covers a broader range of topics like linear algebra, calculus, and statistics? I have advanced math and stats experience but wonder if ML Engineers need even more specialized ML/DL skills.
According to these criteria, MLEs are just responsible for SWE tasks; no deep understanding of machine learning is required; only a general understanding of how to code solutions or possibly optimize models.
This makes me wonder: Are data scientists the ones who only create the models and perhaps offer an interface for MLEs to use them?
A Machine Learning (ML) Engineer is a professional who designs, builds, and deploys machine learning models and systems. Engineers apply their programming, statistics, and machine learning expertise to solve complex problems and enhance technology through automated, data-driven insights.