- 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
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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

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