In North American AI engineer interviews, core questions that frequently appear usually revolve around several key dimensions. Mastering the response skills to these questions can significantly increase the pass rate. Algorithm implementation questions are almost always included. Questions such as writing the backpropagation algorithm by hand or implementing regularized logistic regression directly test mathematical coding skills. When solving these problems, one should explain the changes in matrix dimensions and the gradient calculation process while writing the code. The mathematical derivation of the bias-variance trade-off is a frequently asked theoretical question in machine learning. The best answer should combine specific algorithms, such as how the tree depth in random forests affects the two, and extend to the analysis of the learning curve during actual parameter tuning.
Deep learning architecture design questions such as "How to optimize the memory usage of Transformer on long texts" have almost become standard, requiring a full-stack optimization thinking from sparse multi-head attention to gradient checkpointing techniques. Frequent engineering practice questions include the design of feature storage solutions. When answering, one should compare the impact of the update frequency of real-time features and offline features on model performance and provide scenario-based solutions. Recommendation system problems in system design almost appear in every interview. One should be prepared for the full-chain design from recall and coarse ranking to fine ranking, especially emphasizing how to handle the cold start problem, such as using meta-learning to generate initial user embeddings.
In behavioral interviews, a frequently asked question, "How to deal with the decline in model performance after going live," requires a structured response: first, determine whether it is data drift or concept drift; then, discuss the design of monitoring indicators (such as PSI or changes in feature importance); finally, provide specific implementation plans for online learning or active learning. For the essential team collaboration question, "How to convince colleagues to adopt your solution," the best strategy is to demonstrate the ability to speak with ablation experiment data, for instance, by using controlled variables to prove that new feature engineering can enhance cross-scenario generalization by 15%. In the project deep-dive section, there is a 100% chance of being asked about the basis for technology selection. When answering, present the comparison of alternative options at that time, such as choosing LightGBM over XGBoost because it saved 30% of the feature engineering time in handling categorical features.
When solving whiteboard derivation problems such as deriring the message passing formula of GCN from zero, it is necessary to demonstrate the thinking process deliberately. First, clarify the particularity of the graph data, then analogizes the local connection idea of CNN, and finally mathematizes the symmetry requirements of node aggregation. Programming questions often test the combination of dynamic programming and tree operations, such as finding the longest path with the same value in a binary tree. When solving the problem, it is necessary to pay attention to the completeness test of state definitions. The high-frequency question of model optimization, "How to compress the BERT model", requires a layer-by-layer discussion of the technical combination of knowledge distillation, quantization and pruning, and presents the precision delay trade-off curves at different compression rates. The key to answering these high-frequency questions lies in presenting the complete thinking chain behind technical decisions, rather than the accumulation of fragmented knowledge points.
Release time:2025-04-27
