Design Slack
Real-time team messaging: channels, DMs, presence, history and search for millions of concurrent users.
11 questions on this site are reported in Anthropic software engineer interviews. Each has a full reference design (requirements, back-of-envelope numbers, components, user flows and deep dives with interviewer follow-ups). Each one can be walked through turn by turn and then sat as a mock. Patterns covered: real-time, websockets, fan-out, chat, ranking, caching, social, gpu-batching, streaming, scheduling, kv-cache, tool-calling, context-management, rate-limiting, prompt-caching, consistent-hashing, eviction, replication, hot-keys, frontier, politeness, dedupe, distributed-queues, algorithms, counters, redis, edge, peer-to-peer, chunking, bandwidth, integrity, stragglers, rag, vector-search, hybrid-search, permissions, multi-tenancy, latency-budget, cancellation, prefix-caching, context-assembly, code-completion, ann-indexes, quantisation, segments.
Run an AI mock Anthropic interview →One of these questions, run as a live round in that style, with a debrief at the end.
Attribution comes from public interview guides and candidate reports, not from Anthropic. It indicates where a question has been asked, nothing more.
Real-time team messaging: channels, DMs, presence, history and search for millions of concurrent users.
The home timeline of Twitter, Instagram or Facebook: posts from people you follow, ranked, fresh, and fast for hundreds of millions of users.
Serve a large language model behind an API: queue the requests, batch them onto scarce GPUs, stream tokens back, and keep every accelerator busy without blowing the latency budget.
An AI chat assistant for 100 M weekly users whose streamed answers survive dropped connections, call tools mid-answer, and share scarce GPUs across tiers.
A Redis-like cache as a service: hundreds of nodes, terabytes of memory, sub-millisecond gets, and a cluster that survives losing a machine mid-request.
Fetch billions of pages a month politely, discover new links, skip duplicates, and keep the copy fresh without getting the crawler banned.
A distributed service that decides, in under a millisecond, whether a request from a client is allowed under its quota.
Get a 200 GB checkpoint from one origin onto 10,000 machines in five data centres within minutes, under capped links, verifying every byte.
An assistant that answers employees’ questions from their company’s documents, with citations, only from what each user may open, minutes after edits.
Inline AI code suggestions for 10 M developers, shown within 300 ms of a pause in typing, when most requests are never used.
A managed vector database: 10,000 tenants, 20 B vectors, filtered top-k in under 50 ms at 0.95 recall, and writes searchable in seconds.
A 50–55 minute round, often on infrastructure behind an AI product rather than a consumer app. Two shapes are reported: design from scratch, and reviewing a design document and improving it.
Most of the questions reduce to ordinary distributed systems: queueing, batching, backpressure. Candidates who reach for model internals instead of the infrastructure answer tend to drift.
From public interview guides and candidate reports, and Anthropic’s own interview guidance where it publishes any. Not endorsed by Anthropic. Formats change; treat it as a prior, not a promise.
Large-company loops run 45 to 60 minutes, expect numbers, and spend most of the time on deep dives. Walk through a question to see how a strong candidate paces the hour, sit the same question as a mock until you can produce the requirements, the estimates and the design without looking, then read the interview framework guide for how to structure the hour. The other questions on the site teach patterns that show up in Anthropic interviews under different names.
Questions reported in Anthropic system design interviews include Slack, News Feed, LLM Inference Service, ChatGPT, Distributed Cache, Web Crawler, Rate Limiter and Large File Distribution System, among 11 in total. Between them they test real time, websockets, fan out, chat, ranking and caching. The reports come from public interview guides and candidate accounts, not from Anthropic, so practise the patterns rather than memorising one list.
A 50–55 minute round, often on infrastructure behind an AI product rather than a consumer app. Two shapes are reported: design from scratch, and reviewing a design document and improving it.
Reports say the Anthropic round weighs 4 things. 1. Decomposition: breaking an open problem into parts that can be reasoned about separately. 2. Scale reasoning under real constraints, especially scarce and expensive capacity. 3. Failure modes, overload behaviour and what degrades first. 4. Driving the conversation yourself at staff level, rather than waiting to be prompted. Most of the questions reduce to ordinary distributed systems: queueing, batching, backpressure. Candidates who reach for model internals instead of the infrastructure answer tend to drift.
Practise the questions reported there out loud, under a 45-minute clock: requirements with numbers first, then estimates, a high-level design, and deep dives into the two or three hardest parts. Start with Slack: watch a strong candidate answer it turn by turn, then sit it yourself. Then cover the patterns the round leans on (real time, websockets, fan out and chat) with one more question each.