Anthropic ML System Design Interview Questions
Practice the exact questions companies are asking right now.
"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 GPU inference request batching
Design a system that serves online model-inference requests on GPUs. Requests arrive one at a time from clients, but GPU throughput is far higher when...
Design Model Weight Distribution
Design a system that distributes large machine learning model weight files to a fleet of GPU inference workers. A new model version is published as on...
Design a model downloader
Design a system that distributes machine learning model artifacts from centralized storage to a large fleet of inference servers. The system should su...
Design a high-concurrency LLM inference service
You are designing an LLM inference platform that serves interactive user requests (chat/completions) on GPUs. Goals - Support high concurrency with pr...
How do you handle an LLM agents interview?
You have an interview on your agenda titled “Agents Interview.” Explain how you would approach this interview if it is about designing and evaluating ...
Design a batch inference API
Design an Asynchronous (POST-and-Poll) Inference Service API Design an asynchronous inference service for serving model predictions. A client submits ...
Design a batched inference API
Design an online machine learning inference service that supports dynamic batching. Multiple clients send small synchronous prediction requests to an ...
Design a GPU inference API
Design a scalable, GPU-backed inference API that serves multiple ML models — including large autoregressive models such as LLMs — to internal product ...
Design a low-latency ML inference API
System Design: Low-Latency ML Inference API (Real-Time) Context You are designing an in-region, synchronous ML inference API that sits on the critical...
Design a prompt playground
Design a prompt playground for working with large language models. Users should be able to write prompts, run them against one or more models, compare...
Design a Production ML Serving System
You are given an existing ML-powered production system that serves online user requests. The interview focuses not on changing the model architecture ...
Review an inference API design for scale
System Design Review: A Machine-Learning Inference API at Scale Background You are reviewing a teammate's design document for a production machine-lea...
Design an LLM-based binary classifier
Design a Binary Text Classifier Using Only a Log-Probability Scoring Helper Context You are building a binary text classifier without fine-tuning. You...
Design an inference routing and scheduling layer
System Design: Routing Layer for Heterogeneous Inference Backends (GPU/CPU) Context You are asked to design a routing layer that sits between a user-f...
Estimate VRAM and compare model parallelism
You are a performance engineer reasoning about GPU memory and parallelism for a transformer-like workload whose runtime is dominated by large matrix m...
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...