Machine Learning Engineer Machine Learning Interview Questions
Practice the exact questions companies are asking right now.
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Improve classifier with noisy multi-annotator labels
Problem You are given a text dataset for a binary classification task (label in \{0,1\}). Each example has been labeled by multiple human annotators, ...
Explain NLP/RL concepts used in LLM agents
You are interviewing for an applied ML role focused on LLM agents and retrieval-augmented generation (RAG). Answer the following conceptual questions ...
Explain activations, losses, and Adam
Answer the following ML fundamentals questions: 1) Neural network building blocks - What is a "layer" in a neural network, and what does it compute? -...
Debug transformer and train classifier
Debug and Fix a Transformer Text Classifier, Then Train and Evaluate It Context You inherit a small codebase for a transformer-based text classifier. ...
Explain bias–variance, overfitting, and vanishing gradients
Answer the following ML fundamentals questions: 1. Bias–variance tradeoff: What are bias and variance? How do they relate to underfitting/overfitting?...
Explain leakage, missing data, and common losses
Answer the following traditional ML questions: 1. Data leakage - What is data leakage? - Give 2–3 common examples. - How do you prevent or fi...
Train a classifier and analyze dataset
End-to-End Binary Classifier Workflow (EDA → Modeling → Fairness → Report) You are given a labeled tabular dataset and asked to implement a reproducib...
Explain LLM post-training methods and tradeoffs
You are asked about LLM post-training (after pretraining on large corpora). Explain a practical post-training pipeline for turning a base model into a...
Debug a transformer training pipeline
Diagnose a Diverging PyTorch Transformer Training Run You are given a PyTorch Transformer training pipeline whose loss diverges and validation accurac...
Diagnose Transformer training and inference bugs
Debugging a Transformer That Intermittently Throws Shape/Type Errors and Fails to Converge You are given a Transformer-based sequence model that: - In...
Explain core ML fundamentals and tradeoffs
ML Fundamentals Interview Prompt Answer the following ML fundamentals questions clearly and with practical examples: 1. Bias vs. variance - What ar...
Compare NLP tokenization and LLM recommendations
You’re interviewing for an NLP-focused ML role. Part A — NLP fundamentals: tokenization Explain and compare common tokenization approaches used in mod...
Compare preference alignment methods for LLMs
Question You’re asked to discuss preference alignment approaches for large language models. Task Compare several alignment methods and explain when yo...
Explain FlashAttention, KV cache, and RoPE
You are interviewing for an LLM-focused role. 1. FlashAttention - Explain what problem it solves in transformer attention. - Describe the high-l...
Implement and visualize in-place augmentations
Task: Build a Reproducible Augmentation Pipeline for Grayscale Digit Denoising Context You are training a denoising model on grayscale digit images (e...
Debug a transformer training pipeline
Debugging Plan: PyTorch Transformer Text Model with Mask Errors, Metric Plateau, AMP Crashes, and Nondeterminism Context You are training a Transforme...
Handle cold start, dropout, and training stability
Machine Learning deep dive Answer the following conceptual questions (you may use equations and small examples). A) Recommender systems: cold start 1....
Build and troubleshoot image classification and backprop
CIFAR-like Noisy Dataset: Baseline, Data Quality Plan, and First-Principles Backprop Context: You have a CIFAR-like dataset of 32×32 RGB images, 10–20...
Design a search relevance prediction approach
Search relevance prediction You are asked to predict relevance for an e-commerce search engine (given a user query and a product/document). Prompt 1. ...
Explain overfitting vs underfitting and fixes
Question 1. What are overfitting and underfitting? 2. How can you diagnose each using training/validation metrics? 3. What are common mitigations for ...