Software Engineer Machine Learning Interview Questions
Practice 50 real Machine Learning interview questions for Software Engineer roles. From companies including Amazon, NVIDIA, OpenAI, Microsoft, Google.
"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 normalization, regularization, CTR, imbalance handling
This question evaluates mastery of normalization methods, regularization techniques, click-through-rate modeling, and class-imbalance strategies, with...
Design an Online Experiment
This question evaluates experimental design and causal inference skills for production ML systems, including metric definition, randomization and trea...
Derive MLP shapes and explain PyTorch broadcasting
This question evaluates understanding of tensor shapes, linear layer forward computation, and deep-learning-framework broadcasting semantics. It is co...
Explain RL policy types and modern policy gradients
This question evaluates understanding of reinforcement learning policy types and modern policy-gradient techniques (TRPO, PPO, GAE) alongside attentio...
Debug a Machine Learning Pipeline
Debugging a Sudden Accuracy Drop in a Deployed ML Pipeline Context You are on-call for a production machine learning service. Monitoring alerts show t...
Explain Transformers and QKV matrices
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Build a bigram next-word predictor with weighted sampling
You are given a training set of token sequences (sentences), for example: ` [["a","b","c"], ["a","s","d"]] ` 1) Train a simple next-word prediction m...
How Would You Prevent Hallucinations in an LLM-Based System?
You are interviewing for an AI-focused Software Engineer role at a company building products on top of large language models. During the screen, the i...
Find minimum of unknown convex function
You are given access to an unknown univariate convex function \(f(x)\) defined on a closed interval \([L, R]\) on the real line. - You cannot see the ...
Explain prompt engineering strategies for chatbots
Prompt Engineering for Reliable, Steerable, and Safe Chatbots Context You are designing a production-grade chatbot that must be reliable (consistent, ...
Implement AUC-ROC, softmax, and logistic regression
This question evaluates practical implementation skills for core machine learning components—computing AUC-ROC (including ROC point generation and han...
Compare deep learning framework trends
Compare deep learning framework trends This is an open-ended discussion question with two parts: 1. What high-level trends are happening at the deep l...
Build a Heart Disease Baseline
You are given a tabular dataset for predicting whether a patient has heart disease. The dataset contains a binary target column such as has_heart_dise...
Explain optimization and tensor vs pipeline parallelism
Task: Deep Learning Optimization and Parallelism You are asked to explain optimization techniques commonly used to improve deep learning training and ...
Explain overfitting, regularization, and LLM techniques
This question evaluates understanding of model generalization (overfitting vs underfitting), regularization methods (L1 vs L2), modern LLM techniques ...
Explain learning paradigms, loss, and embeddings
ML fundamentals (oral) Answer the following conceptual questions clearly and with examples: 1. What is supervised learning? What are typical inputs/la...
Explain DPO and construct its training data
You are working on a project to fine-tune a large language model (LLM) using Direct Preference Optimization (DPO). Answer the following: 1. Conceptual...
Explain key ML metrics and techniques
You are asked a set of short conceptual machine learning questions. 1. Confusion matrix and metrics For a binary classification problem: - Def...
Discuss large language models
Discuss large language models LLMs: Advances, Product Integration, Production Challenges, and Risk Mitigation Context You are interviewing for a Softw...
Build and evaluate a Colab classification model
Build and evaluate a Colab classification model End-to-End Tabular Classification Workflow in Google Colab You are asked to design and implement a com...