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."
Implement Multi-Head Self-Attention
Implement a multi-head self-attention module in PyTorch without using torch.nn.MultiheadAttention. Requirements: - Input tensor shape: (batch_size, se...
Build Naive Bayes spam classifier with F1
You are given a text classification dataset for spam detection (binary labels: spam vs not_spam) in a Jupyter notebook environment. Task 1. Preprocess...
Explain core components of reinforcement learning
In reinforcement learning, we model an agent that interacts with an environment over time. The agent observes the state of the environment, takes acti...
Contrast CNNs and fully connected networks
Compare convolutional neural networks (CNNs) with fully connected (dense) networks. Explain: - The structural differences between convolutional layers...
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 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 +...
Explain Logistic Regression Fundamentals
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Explain LLM training and evaluation
LLM Engineering: Training, Alignment, Hallucination Reduction, Evaluation, Monitoring, and Inference Optimization Context You are designing, aligning,...
Explain tokenization and Transformer variants
Tokenization and Transformer Architecture Deep Dive You are asked to explain common tokenization approaches and modern Transformer design choices used...
Implement SGD for linear regression and derive gradients
Prompt You are given a dataset of \(n\) 1D samples \(\{(x_i, y_i)\}_{i=1}^n\), where \(x_i\) and \(y_i\) are real numbers. We want to fit a linear mod...
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...
How would you target promotions to grow consumers?
ML / Growth Scenario You own a system that sends promotion offers (e.g., "$10 off", "free delivery") to consumers to increase growth. Prompt Design an...
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...
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...
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....
Explain XGBoost Parallelism Strategies
Explain How XGBoost Parallelizes Training Scope Describe how XGBoost achieves parallelism: 1. Within a single machine - Histogram-based split findi...
Implement attention and nucleus sampling; compare to top-k
Implement Multi‑Head Attention and Nucleus (Top‑p) Sampling Context You are building core components used in Transformer-based language models. Implem...
Build an imbalanced classification pipeline with sklearn
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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 \(...