Machine Learning Engineer
Practice ML engineering: model training, evaluation, feature pipelines, deep learning, distributed training and serving models in production.
- Questions
- 100
- Interview Qs
- 30
- Judged problems
- 44
- Skills
- 9
About the role
A Machine Learning Engineer turns data and models into working products. Day to day you clean and prepare data, build features, train and tune models, and check them with the right metrics. You work closely with data scientists, backend engineers and product teams to decide what problem the model should solve and how success is measured.
A big part of the job is engineering, not just modelling. You write reliable training pipelines, package models, serve them behind APIs or batch jobs, and make them fast and cheap enough for real traffic. You also watch models after launch: data drift, falling accuracy, latency spikes and bugs where the features in production do not match the features used in training.
Strong ML engineers mix solid fundamentals (statistics, linear algebra, optimisation) with good software habits (testing, version control, reproducibility). Modern ML engineers also work with deep learning, embeddings and large pretrained models, and know when a simple model is the better choice.
Practise by level
Open the full question bank (100 questions) →Mock interview
Screening round
4 questions · 4 min each · pass 50%
mediumTechnical round
5 questions · 4 min each · pass 60%
hardFinal round
6 questions · 3 min each · pass 70%
Coding problems for machine learning engineers
- Best K-Day Sales Windoweasy
- Binary Classifier Metricseasy
- Cumulative Totalseasy
- Min-Max Feature Scalingeasy
- Rotate a Matrix Clockwiseeasy
- Sensor Moving Averageeasy
- Set-Bit Count Tableeasy
- Staircase With Allowed Strideseasy
- Best Match by Cosine Similaritymedium
- Card Velocity and Geo Fraud Rulesmedium
- Count Islands on a Mapmedium
- Count Out-of-Order Pairsmedium
SQL problems
All SQL problems →Skills in this track
Learning roadmap
- 1
Python, maths and data basics
Get comfortable with Python, NumPy and pandas. Revise linear algebra, probability and basic calculus so gradients and loss functions make sense.
- 2
Classical machine learning
Learn linear and logistic regression, trees, gradient boosting and k-NN with scikit-learn. Practise proper train/validation/test splits and cross-validation.
- 3
Evaluation and feature engineering
Master precision, recall, ROC-AUC, PR-AUC, calibration and error analysis. Learn to spot data leakage and build features carefully.
- 4
Deep learning
Train neural networks in PyTorch: optimisers, regularisation, batch norm, CNNs and transformers. Learn to fine-tune pretrained models and use embeddings.
- 5
Production ML
Package and serve models, build feature pipelines, track experiments, monitor drift and run A/B tests. Learn distributed training and inference optimisation.
Top Machine Learning Engineer interview questions
- What is overfitting, how do you detect it, and how do you reduce it?
- Explain precision and recall with a real example. When would you favour one over the other?
- How does gradient descent work, and what is the role of the learning rate?
- What are the training, validation and test sets used for, and how do you split them?
- Why should you build a simple baseline before a complex model? Give examples of good baselines.
- Explain the bias-variance trade-off and how it guides your choice of model.