Road Mates EnterHow to become a Machine Learning Engineer, phase by phase: what you do in each one, how long it takes, and the proof that shows you're done.
6 phases2–3.5 years19 resources
Working daily through Python, linear algebra, probability, and calculus exercises, writing small scripts with NumPy and pandas. By the end, the person can implement gradient descent and basic statistics from scratch without copying code.
You're done when you can show
A GitHub repo of from-scratch implementations like linear regression and gradient descent.
Resources
Taking structured datasets through the full scikit-learn workflow: cleaning, feature engineering, cross-validation, and model comparison. Produces several notebooks that show sound evaluation and avoid data leakage.
You're done when you can show
Published notebooks or a Kaggle profile showing end-to-end tabular modeling projects.
Resources
Building and training neural networks in PyTorch or TensorFlow, including CNNs, transformers, and fine-tuning of pretrained models on GPUs. Produces at least one non-trivial project with documented experiments and results.
You're done when you can show
A repo with a trained deep learning model, experiment logs, and a written results summary.
Resources
Stops working in notebooks and starts wrapping models in APIs, containerizing with Docker, adding tests, CI, monitoring, and experiment tracking on a cloud platform. Ends with a live, reproducible ML service that others can call.
You're done when you can show
A public URL or demo of a deployed model service with its pipeline code on GitHub.
Resources
Preparing a portfolio and resume, practicing coding, ML theory, and ML system design interviews, and applying to roles consistently. Ends with a signed offer for an ML engineering position.
You're done when you can show
A signed offer letter for a machine learning engineer role.
Resources
Owning real ML features end to end inside a team: scoping problems with stakeholders, building data pipelines, running A/B tests, and maintaining models in production. Proves impact through measurable improvements to a product metric.
You're done when you can show
An internal launch doc or performance review citing a shipped model and its metric impact.
Resources
Walk this road with people on it.
Track each phase, post your proofs, and get matched with people in the same phase.