Software Engineer ML System Design Interview Questions
Practice 117 real ML System Design interview questions for Software Engineer roles. From companies including Anthropic, OpenAI, Amazon, NVIDIA, Meta.
"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."
Design AI feature launch and data collection
System Design: From AI Prototype to Production Context Assume you are designing a user-facing AI-powered feature for a web/mobile product. Some decisi...
Design an e-commerce recommendation system
Design an Amazon-Scale E‑Commerce Product Recommendation System Context You are designing a large-scale recommendation system that powers multiple use...
Design a multimodal embedding service
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Design an Agentic Ticket Support System
Design an AI-powered ticket support system. The system must integrate with two existing external systems: 1. Ticket system: Displays all support ticke...
Design a GPU-Efficient Video Service
This question evaluates competency in designing GPU-constrained, production-grade ML serving platforms, emphasizing resource management, job schedulin...
Design an enterprise RAG assistant for internal docs
Scenario Design an enterprise GPT-style assistant that allows employees to ask questions about internal company documents (policies, wikis, specs, tic...
Design a response-ranking ML system
Design a response-ranking ML system System Design: Ranking Candidate Text Responses to Maximize User Satisfaction You are designing an end-to-end mach...
Design scalable, highly available GenAI serving
System Design: Highly Scalable, Highly Available Generative AI Inference Platform Context Design a production-grade deployment for a generative AI tex...
Outline the ML inference and labeling pipeline
Outline the ML inference and labeling pipeline Audio Detection System: ML Inference and Data Pipeline (Model Architecture Out of Scope) Context and As...
Design a Synchronous Text Classification API
Design a Synchronous Text Classification API A trained machine-learning model classifies text. Design the system that exposes this model through synch...
Design a Retrieval-Augmented Generation (RAG) System
Design a retrieval-augmented generation (RAG) system for a production question-answering product. Users ask natural-language questions, and the system...
Design a feature store with CI/CD and reliability
System Design: Feature Store for Offline Training and Low‑Latency Online Inference Context You are designing a feature store to support machine learni...
Build a Mistral-powered RAG agent
Build a Minimal RAG Tool Using the Mistral API Context You have an API token and need to implement a small retrieval-augmented generation (RAG) tool i...
Explain ML compilation optimizations and hardware fit
ML Compiler Optimizations and Platform Targeting Context You are designing a compiler/runtime stack for deep learning workloads that must run efficien...
Build models for housing and wind power prediction
Two-Part Machine Learning Take-Home Part 1 — Binary Classification: "Can Buy" vs "Cannot Buy" Given applicant and market data, design a binary classif...
Design real-time fraud detection under 50ms
Design a real-time fraud detection system for a payments company that processes millions of transactions per day. Requirements: - For each incoming tr...
Design and benchmark optimized inference pipelines
Design and benchmark optimized inference pipelines Accelerating PyTorch Inference: TorchDynamo, Techniques, and Benchmark Design Context You are asked...
Design system to detect privacy-leak records
You are given a very large database that contains user data (both structured fields and unstructured text such as logs, messages, and documents). The ...
Explain ML framework trends
Explain ML framework trends ML Framework Trends, Compilation Pipeline to GPU, and Hardware-Aware Deployment Context You are asked to explain how moder...
Design an LLM Log Parsing Workflow
Design a production workflow that uses an LLM, optionally combined with deterministic parsers, to convert heterogeneous raw log messages into structur...