Machine Learning

Career Roadmaps

Machine Learning Engineer

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 10 July 2026

progress saves in your browser

free personalized PDF study plan.

0%

0/16 items completed 12 critical left

0/100

Critical only

Import File

Stage 1 - Foundations 4 item(s)

Git + Docker - reproducibility is the job Critical

ML fundamentals: supervised learning, evaluation, overfitting, baselines Critical

Python at engineering level: typing, testing, packaging - not just notebooks Critical

Working maths: gradients conceptually, linear algebra notation

Stage 2 - Training 4 item(s)

Classical ML properly (scikit-learn, XGBoost) - still most of production ML Critical

Experiment tracking (MLflow/W&B): configs, metrics, artefacts Critical

One deep learning framework (PyTorch) at working level Critical

Fine-tune one open model (LoRA on a small LLM or vision model)

Stage 3 - Serving & Operating 4 item(s)

Production monitoring: drift, data quality, performance dashboards Critical

Serve a model behind an API: batching, latency budgets, versioning Critical

Training pipelines: reproducible data -> train -> evaluate -> register Critical

Cost engineering: GPU vs CPU, quantisation, caching, right-sizing

Stage 4 - Job-Ready Proof 4 item(s)

CV/LinkedIn target "ML Engineer" naming PyTorch + deployment stack Critical

Passed a mock interview including ML system design Critical

Public end-to-end system: train, serve, monitor - with CI Critical This single repo answers most ML engineering interviews.

Write-up of trade-offs: why this model, latency vs quality, failure modes

Your selections are saved in your browser. Export a personalized PDF print-out any time.

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.

Not sure this is your direction?

Compare every tech role - what you'd build, what it pays, and who it suits - on the roadmaps hub.

Compare all roadmaps