I applied online. The process took one week.
There was just an OA. The whole thing was 70 minutes long. It started with seven multiple-choice questions (not too bad), one LeetCode medium, and two ML from-scratch coding problems. But that coding part took me a solid 50 minutes just struggling with the tool constraints.
The multiple-choice questions covered precision and recall calculations, the differences between bagging and boosting, overfitting-related concepts, and manual forward/backward propagation calculations for neural networks. However, the programming section was highly restrictive. We were limited to using scipy.cdist for KNN and sklearn.tree.DecisionTreeClassifier for decision trees. As a result, I spent the majority of the time checking the documentation. I couldn't understand the restriction on NumPy, as many of my standard NumPy operations were unavailable, and I couldn't come up with alternatives under the time pressure. Consequently, I left the final three problems incomplete.
The following metrics were computed from 1 interview experience for the Pinterest Machine Learning Intern role in California City, California.
Pinterest's interview process for their Machine Learning Intern roles in California City, California is extremely selective, failing the vast majority of engineers.
Candidates reported having very negative feelings for Pinterest's Machine Learning Intern interview process in California City, California.