What ML engineers actually do

An ML engineer turns a model that works on a laptop into something that works for thousands of people, every day, without breaking. You train models, yes. But most of the job is the code around them: getting data in, serving predictions, testing, and watching what happens after launch.

Think of it as software engineering where one of the parts learns from data. That part behaves differently from normal code. It can be right on Monday and quietly wrong on Friday, because the world it was trained on has changed.

The titles overlap, and every company draws the line in a different place. A rough rule: a data scientist asks 'what is true in this data, and what should we do about it?' An ML engineer asks 'how do we make this prediction fast, reliable and cheap in production?'

Data scientists lean on statistics, experiments and clear communication. ML engineers lean on software skills: clean Python, APIs, containers, cloud services, testing. In small teams one person does both. In large teams they hand work back and forth.

Expect to clean and join data, retrain a model when new data arrives, compare it with the current one, and ship it behind an API. You will review pull requests, fix a pipeline that failed at 3 a.m., and explain to a product manager why the new model is better on one group and worse on another.

Since 2023 many ML engineers also build features on top of large language models. That work is less about training and more about prompts, retrieval, evaluation and cost. This track covers both.

Basic Python, comfort with a terminal, and school-level maths: averages, percentages, a feel for what a slope is. You do not need a PhD. You will pick up the linear algebra and statistics you need as you go, one concept at a time.