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."
Answer core ML fundamentals questions
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This question evaluates a candidate's understanding of core machine learning fundamentals—Batch Normalization, optimizer behaviors (SGD, Momentum, RMS...
Compare NLP tokenization and LLM recommendations
This question evaluates a candidate's understanding of NLP tokenization approaches and the ability to design LLM-based recommendation components, asse...
Explain Model Compression Techniques
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For a computer vision product, discuss the following: 1. Explain the core machine learning fundamentals that matter most in vision work, including bia...
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Explain ML and statistical modeling
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Make a hard MoE router differentiable
Make a hard MoE router differentiable Differentiable Routing for Mixture-of-Experts (MoE) Context You are working with an MoE layer that routes each t...
Design an LLM agent with RAG and tools
This question evaluates proficiency in designing LLM-based agents, covering retrieval-augmented generation, tool/API orchestration, safety and evaluat...
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) ...
Train a classifier and analyze dataset
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Implement attention and Transformer with backward pass
Implement Scaled Dot-Product Attention and a Transformer Block (No Autograd) Context: Build multi-head self-attention and a Transformer encoder-style ...
Explain XGBoost's Overfitting Resistance
A single, unpruned decision tree can recursively partition the training set until its leaves describe individual training points, which makes it a hig...
Explain bias–variance, overfitting, and vanishing gradients
This question evaluates understanding of core machine learning fundamentals—specifically the bias–variance tradeoff, overfitting detection and mitigat...
Explain CLIP, contrastive losses, and retrieval limits
Answer the following ML questions in the context of multi-modal (text–video/image) retrieval: 1) How does a CLIP-style model work conceptually (archit...