Machine Learning Engineer Roadmap
You take models from notebook to production - training pipelines, serving, monitoring. Indicative 2026 salaries $95-130k US, usually reached from software engineering or data science rather than as a first job. The role is engineering-heavy: the model is 10% of the system.
By Carl Mills • Last updated • tap any step for guidance and free resources • progress saves in your browser • free printable PDF roadmap poster.
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Machine Learning Engineer roadmap FAQ
ML engineer vs AI engineer vs data scientist?
AI engineers build on hosted LLMs via APIs. ML engineers train, deploy and operate models (including fine-tuning). Data scientists analyse and prototype. ML engineering is the most infrastructure-heavy of the three and typically the hardest first job.
Do I need a GPU and deep learning to start?
No - most production ML is still tabular models (boosting) plus, increasingly, adapting foundation models. Learn the fundamentals on scikit-learn, one deep learning framework at working level, and fine-tune one small open model on a rented GPU for the experience.
What is the realistic route in?
Two doors: software engineer who takes on ML features (most common), or data scientist who owns deployment. Both take 1-2 years. A public repo that trains, serves and monitors a model end to end shortcuts a lot of gatekeeping.
What next once the list is green?
Prove it under pressure: take the free Mock Interview, then check your application signals.
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