Machine Learning Engineering
Ship models that actually work in production.
ML engineers build, train, deploy and monitor machine learning systems at scale.
What the work looks like
Training models, building feature pipelines, deploying inference services, monitoring drift.
Key skills
- Linear algebra & probability
- Python
- Deep learning frameworks
- MLOps
- Software engineering
Common tools
PyTorchscikit-learnMLflowDockerHugging Face
Pros
- Frontier technology
- High-impact work
- Strong global demand
Cons
- Steep maths requirement
- Many roles want a master's
- Field changes rapidly
A 12-month starter roadmap
Months 1–3
- • Linear algebra & probability
- • Python deeply
- • ML basics (Andrew Ng-style)
- • Git
Months 4–6
- • Deep learning with PyTorch
- • Kaggle
- • Deploy a model as an API
- • Read 5 papers
Months 7–12
- • MLOps
- • Transformers
- • One end-to-end ML product
- • Research internship or open source
Reality check
Students like: Teaching machines to do new things.
Students dislike: Experiments that just don't converge.
Hardest part: Bridging research code and production systems.
Common myth: Calling an API isn't ML engineering — fundamentals matter.