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 a Low-Latency GPU Inference Service
Design a Low-Latency GPU Inference Service Design an online inference service backed by GPU servers. Explain how dynamic batching, request deadlines, ...
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 Safe Distribution and Activation of Model Weights
Design Safe Distribution and Activation of Model Weights The source names model-weight deployment as the system-design topic but does not give a scale...
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 batch inference API
Design an Asynchronous (POST-and-Poll) Inference Service API Design an asynchronous inference service for serving model predictions. A client submits ...
How do you handle an LLM agents interview?
This question evaluates an engineer's competency in designing and evaluating LLM-powered agents, covering system architecture, tool and API integratio...
Design a model downloader
This question evaluates a candidate's competency in ML system design and distributed systems, covering model lifecycle management, versioning, integri...
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 a high-concurrency LLM inference service
This question evaluates a candidate's ability to design a high-concurrency LLM inference platform, assessing competencies in GPU utilization and memor...
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...
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 Production ML Serving System
This question evaluates a candidate's competency in operating and scaling ML-powered production systems, focusing on scaling, reliability and fault to...
Estimate VRAM and compare model parallelism
This question evaluates understanding of GPU memory budgeting for large matrix multiplications and the comparative trade-offs between pipeline and ten...
Design a batched inference API
This question evaluates competency in designing scalable, low-latency ML inference systems with dynamic batching, covering system architecture, reques...
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...