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."
Build harmful-content text classifier
This question evaluates a candidate's competence in designing an end-to-end machine learning pipeline for binary text classification, covering data un...
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 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 Transformer Positional Encoding
This question evaluates understanding of positional encoding in Transformer architectures, including how positional information is integrated into tok...
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...
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...
Explain Core ML Concepts
Answer these machine-learning fundamentals questions: 1. Explain the difference between batch normalization and layer normalization, including how eac...
Explain core ML and DL fundamentals
Explain core ML and DL fundamentals Answer the following machine-learning / deep-learning concept questions. Where useful, include the formula, the in...
Explain Core ML Fundamentals
Review ML fundamentals including logistic regression, cross-entropy loss, batch versus stochastic gradient descent, batch size tradeoffs, overfitting,...
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...
Assess LLMs for fraud detection
LLMs in Fraud Detection: Near-Term vs. Long-Term Roles Context You are designing fraud detection for a large-scale digital payments platform with: - R...
Explain XGBoost Parallelism Strategies
Explain How XGBoost Parallelizes Training Scope Describe how XGBoost achieves parallelism: 1. Within a single machine - Histogram-based split findi...
Explain XGBoost depth, regularization, and dropout
ML Conceptual Questions (Onsite) Answer the following: (a) Gradient-boosted decision trees: How does maximum tree depth affect bias/variance, overfitt...
Implement 1D convex minimization in Python
Question Implement, in Python, an algorithm that minimizes a 1D black-box convex function F(x) over a closed interval [a, b]. Assume F is convex (henc...
Represent k-means as an MLP
This question evaluates understanding of how the nearest-centroid step of k-means can be expressed as a fully connected neural network layer with acti...
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 ML fundamentals (activations, CV, vision, sorting)
This question evaluates core machine learning competencies including neural network behavior and activation functions, model evaluation and cross-vali...
Implement correct attention masking
Autoregressive Transformer: Correct Attention Masking with Padding Context: You are implementing decoder self-attention for an autoregressive Transfor...
Explain Collaborative Filtering Approaches
Collaborative Filtering for Recommendations: Approaches, Losses, Regularization, Cold Start, Bias, Evaluation, and Scale Context You are designing a r...
Design a search relevance prediction approach
This question evaluates competency in machine learning for search relevance, including relevance modeling, feature engineering across lexical, semanti...