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."
Debug a Broken Transformer
You are handed a Transformer model implementation (PyTorch-style) that does not train correctly — the loss is not behaving as expected. The model runs...
Defend a Research Direction and Experiment Design
You are interviewing for a research-focused Machine Learning Engineer role at a frontier AI lab. The onsite includes a collaboration / research-discus...
Explain Transformer Attention Fundamentals
In a machine learning fundamentals interview, explain the core mechanics of Transformer models and modern large language model (LLM) training. The int...
Debug a broken Transformer implementation
You are given a small Transformer model implementation (e.g., in PyTorch) plus a tiny training script. The code executes, but the model does not match...
Explain batch inference design
You need to generate predictions for a very large offline dataset, such as all users or all products, once per day using an already trained machine le...
Explain XGBoost's Overfitting Resistance
A single, unpruned decision tree can recursively partition the training set until its leaves describe individual training points, which makes it a hig...
Explain Overfitting and Transformer Attention
You are interviewing for a machine learning engineering role. Answer the following ML fundamentals questions clearly and compare different modeling se...
Debug transformer and train classifier
Debug and Fix a Transformer Text Classifier, Then Train and Evaluate It You inherit a small codebase for a transformer-based text classifier. It ships...
Compare Losses and Explain LoRA
ML Fundamentals: Loss Functions and Low-Rank Adaptation This is a rapid-fire ML fundamentals screen. You are expected to reason precisely about loss f...
Debug Transformer and Add KV Cache
You are given a small decoder-only transformer (GPT-style) implemented in PyTorch for autoregressive (next-token) language modeling. The starter code ...
Analyze vision model failures
For a computer vision product, discuss the following: 1. Explain the core machine learning fundamentals that matter most in vision work, including bia...
Explain LLM lifecycle and trade-offs
Explain the end-to-end lifecycle of a modern large language model. Cover training data collection and filtering, pretraining objectives, transformer a...
Compare NLP tokenization and LLM recommendations
You’re interviewing for an NLP-focused ML role. Part A — NLP fundamentals: tokenization Explain and compare common tokenization approaches used in mod...
Explain LLM post-training methods and tradeoffs
You are asked about LLM post-training (after pretraining on large corpora). Explain a practical post-training pipeline for turning a base model into a...
Debug a transformer training pipeline
Debug a Transformer training pipeline You are handed a PyTorch Transformer encoder–decoder training pipeline that misbehaves. The pipeline includes to...
Discuss ML Project Tradeoffs
You are interviewing for a senior machine learning role and are asked to discuss a past recommendation or prediction project in depth. Use one concret...
Model Product Ranking
You are building a machine learning model for product ranking in an e-commerce marketplace. Given a user, context, and a set of candidate products, ra...
Normalize targets for multitask regression
You are training one machine learning model with a shared representation and two regression heads. Each example has two continuous labels: - Target A ...
Build a model using only pandas/numpy
You are given a tabular dataset as a pandas DataFrame df with: - Feature columns (numeric and/or categorical) - A target column y (either binary class...
Implement CLIP Contrastive Loss
Given a minibatch of paired image and text embeddings, implement the symmetric contrastive loss used in CLIP-style image-text representation learning....