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 production. Build something that trains, serves and monitors a model end to end, not just a model in a notebook.