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 1NN with NumPy
Implement a 1-nearest-neighbor (1NN) classifier from scratch using NumPy, then show that the same decision can be expressed as a neural-network-style ...
Explain Core ML Interview Concepts
You are in a phone screen for an applied scientist / machine-learning engineer role and are asked to verbally explain a set of machine-learning fundam...
Compute entropy and implement 1-NN
You are given two short ML coding problems from a machine-learning engineer screen. Both are implementation-focused but probe whether you understand t...
Debug Sparse Multi-Task Ranking Models
You are a Machine Learning Engineer training a multi-task ranking model for a sparse recommendation funnel at a fintech product. A single model predic...
LLM Fundamentals: Tokenization Design and KL-Regularized SFT
LLM Fundamentals: Tokenization Design and KL-Regularized SFT You are interviewing for a research/ML role on a team that trains and fine-tunes large la...
Build a Payment Fraud Detection Model
You are interviewing for a Machine Learning Engineer role at a FinTech company that processes online card payments. The loop has two technical parts: ...
Debug MiniGPT and Backpropagate Matmul
This is a hands-on PyTorch screen with two independent tasks. You share a code editor with the interviewer and are expected to run the code, read trac...
Implement Backprop for a Tiny Network
Implement and explain the forward and backward pass of a small two-layer neural network for classification — first from scratch with NumPy, then with ...
Design a Double Descent Experiment
You are given a take-home assignment for a mechanistic interpretability / machine learning interview. Design an experiment that clearly demonstrates s...
Evaluate Noisy Data for LLM Post-Training
You are an ML engineer working on post-training a large language model. You are handed a large, noisy dataset assembled from multiple heterogeneous so...
Filter Bad Human Annotations
You are given a large training dataset labeled by human annotators. Some of those annotations are low quality — inconsistent, rushed, the result of mi...
Compute Matrix Prefix Products And Gradients
You are given $N$ square matrices $A[0], A[1], \dots, A[N-1]$, each of shape $D \times D$. Define the inclusive prefix (cumulative) products: $$Y[i] =...
Implement 1NN Embeddings and Forward Pass
Implement a small machine-learning inference pipeline in three parts. You build a vectorized 1-nearest-neighbor (1NN) classifier, a dense neural-netwo...
Explain Transformer and MoE Fundamentals
You are interviewing for a Machine Learning Engineer role. The interviewer is probing your foundational understanding of modern deep-learning and larg...
Implement Masked Multi-Head Self-Attention
Implement the core self-attention module used inside a Transformer encoder, from scratch, in a deep-learning framework of your choice (PyTorch-style p...
Improve Training With Noisy Annotators
You are given a labeled training dataset as a Pandas DataFrame. Each row contains feature columns, an observed label, and an annotator_id identifying ...
Improve classifier with noisy multi-annotator labels
Problem You are given a text dataset for a binary classification task (label in $\{0,1\\}$). Each example has been labeled by multiple human annotator...
Explain ranking cold-start strategies
You are interviewing for a Machine Learning Engineer role on a search / recommendations / ranking team. The interviewer grounds the discussion in a la...
Explain Top-k and Top-p Decoding
In autoregressive text generation, a language model produces a vector of logits for the next token at each step. These logits are converted to a proba...
Derive Backpropagation for Matrix-Product Layers
Consider a neural-network block whose output is produced by multiplying a sequence of trainable weight matrices together, then applying the resulting ...