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How to become an ML Engineer

Model selection, training and evaluation, experimentation, and statistical reasoning about the models you ship.

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The ML Engineer roadmap

Each topic below carries a study note, real reading, and practice questions in the app. A topic unlocks when you pass the one before it, so the order is the path, so you are never guessing what to learn next.

Phase 1Machine learning mL foundations

Supervised learning basics

Supervised learning maps input features to a known label.

Overfitting and bias-variance

A model that memorizes training data but fails on new data is overfitting (high variance).

Baselines first

Always establish a trivial baseline - a constant, a simple rule, or a seasonal average - and measure its error before building anything complex.

Phase 2Data and features

Train/test splits and leakage

How you split data determines whether your evaluation is honest.

Feature engineering

Feature engineering shapes raw data into representations a model can use.

Class imbalance

When one class is rare, accuracy becomes misleading and naive models learn to ignore the minority.

Phase 3Machine learning training

Choosing a model

Match the model to the data and constraints, not to hype.

Hyperparameter tuning

Hyperparameters control model capacity and training dynamics.

Regularization

Regularization trades a little bias for less variance by penalizing model complexity.

Phase 4Machine learning evaluation

Choosing the right metric

The metric must encode the real cost of errors.

Is the improvement real

A higher score on a fixed test set may be noise.

Error analysis

Aggregate accuracy hides where a model fails.

Phase 5Deployment and monitoring

Serving models

Serving turns a trained model into a service under real latency, throughput, and cost budgets.

Detecting data/concept drift

Production data shifts over time, silently degrading a model.

Retraining

When drift is confirmed, retrain on fresh labeled data - but validate the candidate against the current model on a recent held-out window and roll it out gradually via shadow or canary deployment,…

Reading for this path

The primary sources behind the topics above, all free to read.

Coming from another job?

Most people on this path arrived from somewhere else: engineering, analysis, testing, product, support, compliance, design. Onboarding asks what you do today and builds a short starter run out of the gaps, so you begin from what you already know rather than from chapter one. See how it works.

MLOps Engineer

Model CI/CD, registries, monitoring, and drift detection. Keep the path from training to production repeatable and observed.

Multimodal AI Engineer

Text, image, audio, and video together - fusion, cross-modal retrieval, and multimodal integration. Build AI that sees, hears, and reads at once.

NLP Engineer

Text pipelines, classification, extraction, and multilingual systems. Build the language layer of AI products.

Post-Training Engineer

Fine-tuning, RLHF, alignment tuning, and model specialization. Shape base models into products with the behavior you need.

Prompt Engineer

Prompt design, context engineering, and eval-driven iteration. Get reliable, repeatable behavior out of frontier models.

Recommender Systems Engineer

Candidate generation, ranking models, embeddings, and feedback loops. Personalize what every user sees at scale.

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The full study notes, the reading, and the practice questions behind every topic above are in the app. Answer one tonight and you have started.

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