Career path
Machine Learning Engineer
A machine learning engineer builds the systems that put models into production and keep them working. It sits between data science and software engineering — less about discovering a model, more about making one reliable, fast, and maintainable at scale.
What the job actually is
You take models from a notebook to something a real product can depend on. That means building data and training pipelines, serving models behind APIs, and monitoring them for drift and failure once they meet real-world data. A model that works in an experiment but can't be deployed, retrained, or trusted in production is the problem this role exists to solve.
Skills that matter
- Strong software engineering — this is an engineering role first.
- Python and the ML ecosystem — the common toolchain.
- Understanding of ML fundamentals — enough to reason about why a model behaves as it does.
- Data and deployment pipelines — how models get trained and served.
- Monitoring — catching when a live model quietly degrades.
How to switch in
There are two common routes: software engineers who add machine learning fundamentals, and data scientists who strengthen their engineering and deployment skills. Whichever side you start on, the gap to close is the production one — build something that trains, serves, and monitors a model end to end, not just a model in a notebook.