Software Engineer ML System Design Interview Questions
Practice 97 real ML System Design interview questions for Software Engineer roles. From companies including Anthropic, OpenAI, NVIDIA, Amazon, 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 a dynamic rental pricing system
System Design: ML-Driven Nightly Pricing for Short-Term Rentals Context Design a production ML system that recommends (and optionally auto-sets) night...
Design a personalized recommendation system
System Design: Personalized Recommendations for a Consumer App Context Assume you are building the home-feed recommendations for a large consumer app ...
Design a fraud detection system
Design a Real-Time Payment Fraud Detection System Design an ML-powered system that scores each online card-not-present (CNP) payment during authorizat...
Design anomaly detection and response platform
Design an AI-Driven OS Snapshot Anomaly Detection Service Context You are designing a cloud service that ingests operating system (OS) snapshots from ...
Design prompts for JSON-only LLM responses
Design an LLM request that returns strict JSON only Context You are designing a backend call to an LLM for a technical screen (System Design & Enginee...
Implement resilient LLM provider pool
System Design Task: Resilient Multi‑Provider LLM Client Library Context You are designing a client library used by backend services to call external L...
Design a prompt processing backend
Design a prompt processing backend System Design: Background Processing Backend for LLM Prompts Context Design a multi-tenant backend that processes l...
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 personalized discovery recommendations
You are designing a personalized "Discovery" page for an AI-powered search/Q&A platform (similar to Perplexity). The Discovery page should show each u...
Design place-of-interest ML system
Design place-of-interest ML system Design a POI (Places of Interest) Recommendation System Context Design a global POI recommender for a mobile maps/f...
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...
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 a Retrieval-Augmented Generation (RAG) system
Prompt Design a Retrieval-Augmented Generation (RAG) system that answers user questions using an organization’s internal documents (PDFs, wiki pages, ...
Design a recommendation system
Design a recommendation system Design a User–Item Recommendation System Context You are asked to design an end-to-end recommendation service that sugg...
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...
Describe model-to-GPU execution pipeline
Describe model-to-GPU execution pipeline From Model Definition to GPU Execution: Pipeline and Optimizations You are asked to explain the end-to-end pa...
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...
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...
Design an ML inference orchestration platform
System Design: ML Inference Orchestration Platform Context You are designing a multi-tenant platform that exposes several ML models as independent ser...
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...