Machine Learning Engineer ML System Design Interview Questions
Practice 194 real ML System Design interview questions for Machine Learning Engineer roles. From companies including OpenAI, Meta, Amazon, Snapchat, 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."
Design a recommendation system for Jira issues
This question evaluates end-to-end ML system design competencies for recommendation services in an issue-tracking platform, covering data and labeling...
Design a system for LinkedIn Skills
Design an ML system for “LinkedIn Skills”. The system should infer and/or recommend skills for members, and support downstream use cases like search/r...
Design LinkedIn Learning course recommendations
Design a mini ML system to recommend LinkedIn Learning courses to a user. Product goal: - Recommend courses that help the user succeed in their job se...
Design RL-based spending limit policy
RL System Design: Per‑User Spending Limits You are designing a reinforcement learning (RL) system to set per-user spending limits in a payments/risk c...
Design an ML search system
Design an ML‑Powered Enterprise Document Search System Context You are designing a multi‑tenant enterprise search system that indexes documents from m...
Design a model to choose dynamic K
Problem You are building a recommender system with a two-stage ranking pipeline: 1. Candidate retrieval (recall): fetch top-K candidates for a request...
Design a video VLM end-to-end
This question evaluates a candidate's competency in end-to-end design of video vision-language models (VLMs), covering data strategy, model architectu...
Design a Skills inference system
This question evaluates the ability to design an end-to-end machine learning system for skills inference, including data source integration, labeling ...
Design an ads ranking ML system
This question evaluates a candidate's ability to design a low-latency ads ranking machine learning system, including feature engineering and freshness...
Design a DNA-sequence optimization loop
This question evaluates a candidate's ability to design an end-to-end ML-driven experimental optimization loop for DNA sequence engineering, including...
Explain parallelism and collectives in training
Parallelism strategies and communication in large-scale training You are designing a distributed training setup for very large neural networks that ca...
Design a Real-Time Feature Store
Design a real-time feature store for machine learning systems used in ads or recommendation ranking. Your design should support both: - Online inferen...
Design a RAG-based assistant service
This question evaluates system-design and machine-learning engineering competencies related to Retrieval-Augmented Generation, including architecture ...
Optimize vector semantic search for an assistant
This question evaluates a candidate's competency in designing production-grade vector semantic search systems, including embedding model selection and...
Design a weapon-sale ad detection system
This question evaluates a candidate's competence in end-to-end machine learning system design, covering multimodal signal integration (text, images, b...
Implement a trie-based tokenizer
Design and Implement a Trie-Based Subword Tokenizer for LLM Pretraining Context You are building a subword tokenizer for a large-scale LLM pretraining...
Explain Transformers and deploy an LLM safely
Answer the following LLM-focused questions. 1) Transformer basics - What problem does the Transformer architecture solve compared with RNNs? - Explain...
Optimize Model Serving Under 200ms
A data science team gives you a trained model and asks you to deploy it as an online inference service. The requirement is that a single prediction mu...
Design a grounded voice assistant
This question evaluates understanding of grounding strategies for large language models, causes and mitigation of hallucinations, response quality eva...
Approach an ambiguous business problem
This question evaluates a candidate's ability to handle ambiguity in ML system design by assessing skills in stakeholder communication, problem scopin...