Software Engineer Machine Learning Interview Questions
Practice 43 real Machine Learning interview questions for Software Engineer roles. From companies including NVIDIA, Amazon, OpenAI, Microsoft, 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."
Debug a GRPO training loop and explain ratios
You are given a simplified implementation of a GRPO (Group Relative Policy Optimization) training step for an RLHF-style policy model. The training is...
When should products use AI?
A product-oriented interview asks you to discuss AI adoption in software products. Explain how you would decide whether a feature should use AI or a t...
Implement and Debug Backprop in NumPy
Two-Layer Neural Network: Backpropagation and Gradient Check (NumPy) Context You are implementing a fully connected two-layer neural network for multi...
Find Optimal Piecewise Constant Regression Parameters
Given a dataset of one-dimensional training examples \((x_i, y_i)\) for \(i = 1, \dots, n\), fit a one-split piecewise constant regression model: \[ \...
Design sequence decoding with greedy and beam search
Next-Token Decoding: Greedy and Beam Search Context You are given a probabilistic next-token dictionary D that maps each token t to a dictionary of ca...
Implement and derive backprop from scratch
Tiny Neural Network (From First Principles): Binary Classification Context You will implement and analyze a minimal neural network for binary classifi...
Explain LLM fine-tuning and generative models
Machine Learning fundamentals (LLM / Generative AI track) You are interviewed for an ML role focused on LLMs and generative AI. Part A — LLM fine-tuni...
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...
Design an Online Experiment
You are asked to design a statistically sound experiment to evaluate whether a new ride-dispatch or scheduling policy improves product performance. De...
Implement and analyze custom attention
Implement Scaled Dot-Product Attention in PyTorch (from scratch) Context You will implement a numerically stable, vectorized scaled dot-product attent...
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...
Design Restart Strategy for Oracle Solver
You have an oracle-style math reasoning solver. On each independent run, the time to produce a correct answer is a random variable T with known distri...
Choose models for trading tasks
You are given several modeling options for quantitative trading or pricing work: linear regression, convolutional neural networks, transformers, and r...
Explain normalization, regularization, CTR, imbalance handling
You are interviewing for an applied ML role. Answer the following fundamentals clearly and concretely (you may use equations and practical examples): ...
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...
Implement multi-head self-attention correctly
Implement Multi-Head Self-Attention (from scratch) Context You are given an input tensor X with shape (batch_size, seq_len, d_model). Implement a mult...
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...
Debug a failing ML classifier
Debugging a Churn Prediction Pipeline With Poor Generalization Context You are evaluating a binary churn prediction system with: - Training ROC AUC: 0...
Derive MLP shapes and explain PyTorch broadcasting
You are given a standard MLP layer (fully connected layer) used in deep learning. 1. Write the forward computation for a linear layer with bias. 2. Gi...
Explain RL policy types and modern policy gradients
Machine Learning Fundamentals (RL + Attention) Part A — Reinforcement Learning 1. Define on-policy vs off-policy learning. - What makes an algorith...