Machine Learning Engineer Machine Learning Interview Questions
Practice 217 real Machine Learning interview questions for Machine Learning Engineer roles. From companies including Amazon, OpenAI, Snapchat, Pinterest, TikTok.
"I got asked a hardcore MCM DP question and I saw it on PracHub as well. Solved that question in 5 minutes. Without PracHub I doubt I could solve it in 5 hours. Though somehow didn't get hired, perhaps I guess I solved it too fast? /s"
"Believe me i'm a student here jn US. Recently interviewed for MSFT. They asked me exact question from PracHub. I saw it the night before and ignored it cause why waste time on random sites. I legit wanna go back and redo this whole thing if I had chance. Not saying will work for everyone but there is certainly some merit to that website. And i'm gonna use it in future prep from now on like lc tagged"
"10 years of experience but never worked at a top company. PracHub's senior-level questions helped me break into FAANG at 35. Age is just a number."
"I was skeptical about the 'real questions' claim, so I put it to the test. I searched for the exact question I got grilled on at my last Meta onsite... and it was right there. Word for word."
"Got a Google recruiter call on Monday, interview on Friday. Crammed PracHub for 4 days. Passed every round. This platform is a miracle worker."
"I've used LC, Glassdoor, and random Discords. Nothing comes close to the accuracy here. The questions are actually current — that's what got me. Felt like I had a cheat sheet during the interview."
"The solution quality is insane. It covers approach, edge cases, time complexity, follow-ups. Nothing else comes close."
"Legit the only resource you need. TC went from 180k -> 350k. Just memorize the top 50 for your target company and you're golden."
"PracHub Premium for one month cost me the price of two coffees a week. It landed me a $280K+ starting offer."
"Literally just signed a $600k offer. I only had 2 weeks to prep, so I focused entirely on the company-tagged lists here. If you're targeting L5+, don't overthink it."
"Coaches and bootcamp prep courses cost around $200-300 but PracHub Premium is actually less than a Netflix subscription. And it landed me a $178K offer."
"I honestly don't know how you guys gather so many real interview questions. It's almost scary. I walked into my Amazon loop and recognized 3 out of 4 problems from your database."
"Discovered PracHub 10 days before my interview. By day 5, I stopped being nervous. By interview day, I was actually excited to show what I knew."
"I recently cleared Uber interviews (strong hire in the design round) and all the questions were present in prachub."
"The search is what sold me. I typed in a really niche DP problem I got asked last year and it actually came up, full breakdown and everything. These guys are clearly updating it constantly."
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 Transformer and Fine-Tuning Basics
You are interviewing for an AI-focused engineering internship. Explain the following: 1. What is the difference between a transformer model and an emb...
Implement Prefix Products and Their Backward Pass
Let x[0..n-1] be a sequence and define inclusive prefix products by y[i] = x[0] x[1] ... * x[i]. Work through the following variants. State how zero...
Explain and test completion-rate gaps
In a food delivery marketplace, alcohol-related orders have a lower order completion rate than non-alcohol orders. Answer the following: 1. Propose se...
Diagnose Transformer training and inference bugs
Debugging a Transformer That Intermittently Throws Shape/Dtype Errors and Fails to Converge You inherit a Transformer-based sequence model (decoder-on...
Compare Losses and Explain LoRA
ML Fundamentals: Loss Functions and Low-Rank Adaptation This is a rapid-fire ML fundamentals screen. You are expected to reason precisely about loss f...
Build a baseline classification model from messy data
In a live notebook (e.g., Jupyter), you are given a messy, real-world tabular dataset for a binary classification problem. Data characteristics - Targ...
Compute Gaussian Probability and Regression Coefficients
You are given two independent standard normal random variables, X and Y. 1. Compute P[X > 3Y]. 2. In ordinary linear regression with design matrix X i...
Debug a transformer training pipeline
Debug a Transformer training pipeline You are handed a PyTorch Transformer encoder–decoder training pipeline that misbehaves. The pipeline includes to...
Explain self-attention, LoRA, Adam vs SGD, ViT
Answer the following ML/Deep Learning interview questions: 1) Describe self-attention in Transformer models. What are the queries, keys, and values, a...
Explain overfitting, dropout, normalization, RL post-training
Machine Learning fundamentals Answer the following: 1. What is overfitting? How can it be mitigated in machine learning? 2. Narrowing to deep learning...
Analyze CTR Data and Train Model
This question evaluates end-to-end supervised machine learning competencies for tabular click-through rate prediction, including exploratory data anal...
Implement bagging with decision trees
Implement a simple bagging (bootstrap aggregating) classifier that uses decision trees as base learners. You are given a template with a DecisionTree ...
Model Product Ranking
You are building a machine learning model for product ranking in an e-commerce marketplace. Given a user, context, and a set of candidate products, ra...
How do you choose a model?
This question evaluates skills in model selection, task and business-objective definition, data-property analysis, production constraint reasoning, ba...
Explain bias-variance and evaluate a classifier
You are interviewing for an Applied Scientist internship. Answer the following ML foundations questions. 1) Bias–variance - Define bias and variance i...
Debug Transformer and Add KV Cache
This question evaluates debugging and implementation skills for transformer-based autoregressive language models, focusing on attention mechanics, pos...
Explain NLP/RL concepts used in LLM agents
This question evaluates proficiency in transformer-based NLP, embedding methods, LLM agent architecture and evaluation, retrieval techniques for RAG, ...
Design Features for Residual Volatility
This question evaluates competency in feature engineering for financial time-series, volatility forecasting after removing systematic market effects, ...
Implement Naive Bayes classifier from scratch
Implement a Naive Bayes classifier from scratch (you may use NumPy). Write a class with: - fit(X, y): estimate class priors and feature likelihood par...