Big Tech platforms Machine Learning Engineer Interview Loop
A machine learning engineer interview at a big platform is a software engineering loop with ML design added. It isn't a data science loop with some code. At Meta, Google and Amazon, there's a recruiter or written pre-screen, then a technical screen with two medium algorithm problems. The onsite has four to six rounds: two coding, one or two ML system design, and one behavioral. Meta has added an AI-assisted coding round, where you extend a multi-file codebase with an LLM. The algorithms bar keeps moving toward the software engineering bar. Candidates who over-prepare ML and under-prepare algorithms fail the first screen. Amazon adds Leadership Principles in every round and a Bar Raiser.
Practice by round
Coding basics at speed, like arrays, dictionaries, trees and complexity. Then the ML basics a screen asks about: bias and variance, loss functions, regularization, and how you'd evaluate a classifier.
| Question | Difficulty |
|---|---|
| Data Structures in Python | Easy |
| Join and Relate | Easy |
| Sorted Vectors | Easy |
| Python List | Easy |
| Missing Values PostgreSQL | Easy |
| Dealing with Missing Values | Easy |
| Efficient Search in Integer Arrays | Easy |
| Efficient Median Finder | Easy |
Medium and hard coding, like topological sort, binary-tree traversal, the longest path in a DAG and string reversal. There's also the SQL and Python data handling that ML pipelines run on. Handle edge cases before the interviewer names them, and catch your own bugs. Both get scored.
| Question | Difficulty |
|---|---|
| Flattening Nested Lists | Medium |
| Max Value Key Sorting | Medium |
| Monotonic Array Checker | Medium |
| Array Into Bins | Medium |
| Randomized Distribution Generator | Medium |
| Bernoulli Matrix Column Normalization | Medium |
| Linear Fit Algorithm | Medium |
| Balancing Special Text | Hard |
| Nested JSON Flattening | Hard |
| Longest Path in DAG | Hard |
| Maximum Path-Sum in Tree | Hard |
| Binary Tree Traversal | Hard |
| Graph Topological Sort | Hard |
| Linked List Stack | Hard |
| Largest Perimeter | Hard |
ML system design, which keeps coming back to recommendation and ranking in the company's own products. Examples are People You May Know, a short-form video recommender, an ad-platform recommender, a multi-modal recommender, and feed ranking with its impact experiment. Frame the task and design the data path. Pick a model and explain why. Define offline and online evaluation, then talk about serving under latency and cost limits.
| Case study | Difficulty |
|---|---|
| AI Customer Service Quality Metrics | Medium |
| Location-Based Recommendation System | Medium |
| AI-Driven Comment Engagement | Medium |
| Short-Form Video Recommendation System | Hard |
| Feed Ranking Impact Experiment | Hard |
ML breadth with follow-ups: bias and variance, how boosting works, why random forests generalize, logistic regression coefficients, what to do with few labeled instances, and choosing K.
| Question | Difficulty |
|---|---|
| Machine Learning Concept | Easy |
| Categorical Dependent Variable | Easy |
| Popular Naive Bayes Model | Easy |
| Encode Categorical Features | Easy |
| Feature Engineering | Easy |
| NFL Games | Easy |
| High Bias High Variance | Medium |
| Selecting K | Medium |
| The 5 Vs | Medium |
| Bounding Box Regressor | Medium |
| Predict Next Purchase | Medium |
| Offline Wins, Online Loses | Medium |