System Design Interview Questions

Practice 1,336 real system design interview questions from Meta, OpenAI, Amazon, Anthropic and Google. They cover the usual ground, API design, data modelling and storage choice, caching, sharding and replication, queues and event streams, rate limiting, consistency and failure handling, and capacity estimates, but applied to concrete products rather than abstractions: a payment dashboard that scales with readers, a scheduler for opaque long-running GPU jobs, a conversational flight search and price-tracking service. 459 are rated hard, the highest share of any category on the site, and 611 were asked in onsite rounds against 511 in technical screens. 1,082 come from Software Engineer loops, with the remainder from Machine Learning Engineer, Data Engineer and Backend Engineer interviews. Each one carries a written solution that works through the trade-offs an interviewer expects you to raise unprompted.

1.3k Questions 268 Companies09.10.2026
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Frequently Asked Questions

How difficult are system design interview questions compared with other technical rounds?
System design interview questions are among the most open-ended and evaluative rounds, typically harder in assessment style than a single coding problem because they judge breadth, judgment, and tradeoff reasoning rather than only correctness. For mid-level roles you’ll be expected to scope a sensible end-to-end design and justify choices; senior and staff candidates must show capacity planning, failure-mode analysis, and operational tradeoffs. Companies that weight system design heavily, such as Meta, Google, Amazon, and OpenAI, expect crisp tradeoff language and pragmatic constraints. Success requires structured thinking, concrete numbers, and clear prioritization under time pressure.
Where in a typical interview loop does system design appear and how long are those rounds?
System design usually appears during the onsite or panel stage of a technical loop, most often as a dedicated 45–60 minute session; for senior candidates it can also show up in phone screens or as multiple rounds including a design-to-build follow-up. At Google, Meta, Amazon and OpenAI you can expect one or two design-focused interviews inside a multi-hour loop that also contains coding and behavioral rounds. Interviewers look for requirement framing in the first 5–10 minutes, high-level architecture in the next 25–35 minutes, and targeted deep dives and tradeoffs in the final 5–15 minutes.
How long should I prepare for system design interviews to be competitive?
Preparation time depends on level: mid-level candidates typically need four to eight weeks of focused work to internalize frameworks and practice 10–15 canonical designs; senior and staff candidates commonly prepare three to four months to deepen distributed systems, reliability patterns, and operational experience. Aim for weekly deliberate practice that mixes mock interviews, whiteboard runs, and written designs, with at least a handful of timed, full-length mock sessions. If you’re targeting companies like OpenAI or Meta where ML and scale-specific design are common, include targeted practice on model-serving and data pipelines.
What key subtopics should I master for system design interviews in 2026?
Master the end-to-end flow: requirement elicitation, API design, data model, capacity planning and scaling, storage choices, caching, load balancing, partitioning and consistency tradeoffs, and reliability/operational concerns such as monitoring and SLOs. For AI-focused roles at OpenAI and Google, add model serving, feature stores, model metadata, and evaluation pipelines. For Amazon-style questions emphasize cost, availability, and customer impact; for enterprise SaaS companies like Rippling, include multi-tenant isolation, RBAC, compliance, and auditability. Be fluent with concrete numbers, failure modes, and caching and database tradeoffs.
What are the standout tips and common pitfalls to avoid in system design interviews?
Begin by clarifying scope and constraints, then propose a simple end-to-end design before digging into optimizations; interviewers reward sensible scoping and justified tradeoffs more than encyclopedic feature coverage. Use capacity estimates and latency/bandwidth numbers to make design decisions concrete. Avoid common pitfalls: over-designing irrelevant features, skipping failure modes and monitoring, being vague about APIs or data models, and saying “throw a queue at it” without explaining why. For security- or compliance-sensitive domains, explicitly address privacy and audits. Close by summarizing tradeoffs and next steps to show product and operational awareness.

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Meta95OpenAI91Amazon61Anthropic45Google45DoorDash42Rippling41Microsoft35
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Software Engineer1213Machine Learning Engineer41Data Engineer29Backend Engineer22