Machine Learning Engineer Machine Learning Interview Questions
Master your tech interview with our curated database of real questions from top companies.
Explain key ML theory and techniques
Onsite Machine Learning Engineer: Mixed Topics You are asked to answer concisely but with depth across the following topics: 1) XGBoost Parallel Compu...
Build and evaluate click prediction models
Click-Through Rate (CTR) Prediction: Build, Compare, and Justify Models Context You are given a tabular dataset for binary click prediction (click = 1...
Compare float types and design ablation
Floating-point types and ablation study design You are training deep neural networks on modern accelerators that support multiple floating-point forma...
Explain GRPO-style training for diffusion models
You are given a pretrained image diffusion model that generates images conditioned on text prompts (e.g., a text-to-image model). You now want to fine...
Explain weight initialization methods and goals
Explain why weight initialization matters in deep neural networks. Then describe common initialization methods (such as random normal/uniform, Xavier/...
List hyperparameter tuning methods
Describe common methods for hyperparameter tuning in machine learning. For each method, explain: - How it works conceptually. - Its advantages and dis...
Contrast CNNs and fully connected networks
Compare convolutional neural networks (CNNs) with fully connected (dense) networks. Explain: - The structural differences between convolutional layers...
Analyze attention complexity and improvements
In the context of Transformer-style models, analyze the computational complexity of self-attention. Assume a sequence length of \(n\) and hidden dimen...
Compare decision trees and random forests
Compare decision trees and random forests. In your answer, discuss: - How a single decision tree is built and its main advantages and disadvantages. -...
Explain vanishing gradients and activations
Explain the vanishing gradient problem in deep neural networks. In your answer: - Describe how backpropagation works at a high level and why gradients...
Describe overfitting and L1/L2 regularization
Define overfitting in machine learning and explain why it is harmful. Then describe L1 and L2 regularization: - How each one modifies the loss functio...
Explain the bias–variance trade-off
Explain the bias–variance trade-off in supervised learning. In your answer, cover: - What bias and variance mean in the context of a prediction model....
Define QKV for recommender cross-attention
You are designing a deep-learning–based recommendation system that uses a Transformer-style cross-attention block to model the interaction between a u...
Explain Transformers and MoE in LLMs
You are interviewing for a role working with large language models (LLMs). Explain the following concepts and how they relate to building and scaling ...
Explain transformer architecture and variants
Technical Screen: Explain the Transformer Architecture Scope Provide a structured deep-dive into Transformers. Your explanation should cover theory, s...
Explain core ML concepts and design choices
ML Fundamentals — Interview Questions Instructions Answer the following five ML fundamentals questions. Use precise definitions, equations, and concis...
Implement greedy and beam decoding
Implement Greedy and Beam Search Decoders over Next-Token Probabilities Context You are given a directed token graph represented as a Python dictionar...
Explain modeling challenges and fixes
Model Development Challenges: Detection, Alternatives, Solution, Evidence Context: In a technical screen for a Machine Learning Engineer, you are aske...
Implement 1D convex minimization in Python
1D Black-Box Convex Minimization (Gradient-Free) Task Implement in Python an algorithm to minimize a 1D convex function F(x) over a closed interval [a...
Explain LLM architecture, tuning, evaluation
LLM Architecture, Positional Embeddings, Fine-Tuning (PEFT), Regularization, and Evaluation Context You are interviewing for a Machine Learning Engine...