Machine Learning Engineer Machine Learning Interview Questions
Practice 192 real Machine Learning interview questions for Machine Learning Engineer roles. From companies including Amazon, OpenAI, Snapchat, Apple, 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."
Explain surprisal and its units
You are discussing a language-modeling / NLP project. The interviewer asks about surprisal. 1. Define surprisal for an event/token with probability \(...
Explain Transformers, activations, and training optimization
Explain Transformers, activations, and training optimization Modern Deep Learning: Conceptual Questions (ML Engineer Take-home) You are preparing for ...
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 LLM architecture, tuning, evaluation
LLM Architecture, Positional Embeddings, Fine-Tuning (PEFT), Regularization, and Evaluation Context You are interviewing for a Machine Learning Engine...
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 ML fundamentals (activations, CV, vision, sorting)
You are asked several ML-fundamentals questions. Answer each clearly and concisely, including key assumptions, trade-offs, and what you would do in pr...
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 ...
Design an LLM agent with RAG and tools
You’re asked to describe how you would build an LLM-based agent that can converse with a user (e.g., an interviewer) and answer questions using an int...
Explain why LLMs produce hallucinations
Large language models (LLMs) are known to "hallucinate"—that is, they sometimes produce fluent, confident answers that are factually incorrect or unsu...
Explain Core ML Fundamentals
During a machine learning screening, the candidate was asked a set of rapid-fire fundamentals questions. Answer the following in a concise but correct...
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 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 Transformer Positional Encoding
In a Transformer-based sequence model, explain why positional encoding is needed. Describe how positional information is incorporated into token repre...
Evaluate TPR/FPR, sigmoid, and activations
You have a 70-minute assessment with several ML-fundamentals multiple-choice questions. Answer the following (show calculations where applicable). 1) ...
Explain core ML fundamentals
Explain core ML fundamentals ML Fundamentals — Onsite Interview Task Context: Answer the following fundamentals as if in an onsite ML Engineer intervi...
Explain LLM fundamentals and trade-offs
Explain LLM fundamentals and trade-offs LLM Fundamentals — Onsite Interview Task Context: Assume a modern transformer-based LLM. Provide precise, conc...
Explain modern modeling and alignment methods
In a machine learning technical interview, explain the following topics in depth. For each one, describe the problem it solves, the core idea, key tra...
Explain Core ML Concepts
Answer these machine-learning fundamentals questions: 1. Explain the difference between batch normalization and layer normalization, including how eac...
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....
Compare convolutions and transformers
Compare CNNs and Transformers Task Explain the key differences between convolutional neural networks (CNNs) and transformer architectures. Specificall...