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."
Compute Sentence Similarity
Given two text inputs, design and implement a method to compute their semantic similarity. You may use either of the following approaches: 1. Encode e...
Compare convolutions and transformers
Compare CNNs and Transformers Task Explain the key differences between convolutional neural networks (CNNs) and transformer architectures. Specificall...
Machine Learning Fundamentals: Tree Models, Training, Evaluation, and Embeddings
Machine Learning Fundamentals: Tree Models, Training, Evaluation, and Embeddings This is a concept-check round for an early-career ML engineer. The go...
Explain PPO and Transformer basics
PPO, Bellman Equations, On-/Off-Policy Learning, and Transformer Basics Context: You are interviewing for a machine learning role with emphasis on rei...
Explain Layer Normalization in Transformers
Layer Normalization in Transformers: Placement, Gradients, and Practical Trade-offs Task Explain Layer Normalization (LayerNorm) as used in Transforme...
Explain challenges in training multimodal LLMs
Machine Learning discussion Answer conceptually (no code). Assume you are training or adapting a multimodal large model (e.g., text + image, or text +...
Test whether two user populations differ
Problem You are given two groups of users: - Group A: North America users - Group B: Europe users Each user has a vector of continuous features (e.g.,...
Explain 3D geometry data
Explain 3D geometry data 3D Geometry Data: Representations, Preprocessing, Modeling, and Serving Prompt You are working with 3D geometry data in ML pi...
Implement universal adversarial attack on GPT-2
Robustness Evaluation: Universal Adversarial Prompts for GPT-2 You are in a Machine Learning Engineer interview. Explain how you would build a control...
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
This question evaluates understanding of Transformer self-attention in the Machine Learning domain, testing the ability to analyze time and space comp...
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....
Design approach for class imbalance
Design approach for class imbalance Imbalanced Binary Classification: Learning, Evaluation, and Model Selection Context You are training a binary clas...
Design a News-Filtering Prompt
You are acting as the coach of an Olympic champion. The athlete receives many news articles every day, and you want to use a large language model to f...
Explain key ML theory and techniques
Explain key ML theory and techniques This Amazon Machine Learning Engineer onsite covers a breadth of core ML theory and applied modeling. Be ready to...
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...
Explain LLM architecture, tuning, evaluation
LLM Architecture, Positional Embeddings, Fine-Tuning (PEFT), Regularization, and Evaluation Context You are interviewing for a Machine Learning Engine...
Explain imbalance, metrics, bias-variance, Transformers vs. CNNs
Question You are given a highly imbalanced binary classification problem in a fraud-detection setting (roughly 1% positives). Walk through the core ML...
Explain attention variants and their tradeoffs
You are asked to explain and reason about modern Transformer attention mechanisms. 1) Scaled dot-product attention - Define the operation mathematical...