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System Design Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 1201–1230 of 1311 results
- TeslaMediumSoftware EngineerDesign synchronization for water molecule assembly
This question evaluates concurrency and synchronization skills, including thread coordination, use of locks/semaphores/condition variables, and reasoning about safety and liveness properties in concurrent systems.
System DesignHR Screen - TeslaHardSoftware EngineerFrom a Monorepo Commit to an Over-the-Air Vehicle Update, and Choosing What to Build
Trace everything that happens between an engineer pushing a commit to a monorepo and the resulting software being installed on customers' cars, then explain how the monorepo decides which targets to build and test and in what order. It tests CI and release gating, artifact provenance, staged over-the-air rollout, safe in-vehicle installation, and dependency-graph builds.
System DesignOnsite - TikTokHardData EngineerDesign a Warehouse for Key Metrics
This question evaluates a data engineer's skills in dimensional modeling, data warehousing, and analytics infrastructure, covering facts and dimensions, grain definition, surrogate keys, partitioning and clustering strategies, and operational concerns like deduplication, late-arriving events, null/anonymous users, and slowly changing dimensions.
System DesignTechnical Screen - TikTokMediumSoftware EngineerDesign a distributed key-value store
This problem tests your ability to design a scalable, fault-tolerant distributed key-value store, focusing on partitioning strategies, replication, and handling node failures.
System DesignTechnical ScreenDistributed SystemsScalabilityHashing - TikTokHardSoftware EngineerDesign a high-concurrency ticketing system
TikTok software-engineer system-design interview question: design a high-concurrency ticketing / flash-sale system that survives millions of concurrent purchase attempts with zero oversell. It tests admission control and fairness (virtual waiting room, FIFO vs lottery), atomic inventory allocation with TTL reservations, anti-bot defenses, idempotent order creation, hot-key sharding, the data model and APIs, caching, message queues and delivery semantics, consistency and reconciliation, failure handling, capacity planning, SLOs, load testing, and monitoring.
System DesignTechnical Screen - TikTokHardSoftware EngineerDesign and explain your project architecture
A TikTok software-engineer system-design screen that asks you to walk through one of your recent projects end-to-end: architecture diagram, components and data stores, sync vs. async data flows and protocols, scaling and capacity planning, fault tolerance, backpressure, idempotency, observability, and security. It also probes the design trade-offs you made, the real failure modes you hit, the measurable impact, and what you would redesign. Includes a fully worked reference answer plus the scoring rubric.
System DesignTechnical Screen - TikTokMediumSoftware EngineerDesign low-latency large-scale hotel booking system
This question evaluates understanding of large-scale backend and distributed system design, including scalability, high availability, consistency models, concurrency control to prevent double-booking, hotspot mitigation, and protocol trade-offs for low-latency booking and availability flows.
System DesignTechnical Screen - TikTokHardSoftware EngineerDesign overload protection with load shedding
Design overload protection with load shedding evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - TikTokHardSoftware EngineerDesign tables from metrics
This question evaluates analytics data modeling and event-driven warehousing skills, including schema design, metric derivation, partitioning/clustering strategies, and data-quality concerns such as deduplication, late arrivals, and idempotency.
System DesignTechnical Screen - TikTokHardSoftware EngineerExplain SRE architecture and troubleshooting scenarios
This question evaluates a candidate's competency in SRE architecture and reliability engineering, covering Kubernetes fundamentals, pod failure troubleshooting, SLO/SLA/SLI reasoning, multi-layer latency diagnosis, and SQL versus NoSQL trade-offs within the System Design domain.
System DesignTechnical Screen - TikTokHardSoftware EngineerExplain Your System Architecture
Explain Your System Architecture evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - TikTokHardSoftware EngineerImprove and measure service performance
This question evaluates performance engineering and observability skills—specifically SLIs/SLOs definition, instrumentation and profiling, load-testing, bottleneck identification, and optimization trade-offs for stateless HTTP microservices.
System DesignTechnical Screen - ToastHardSoftware EngineerDesign a Restaurant Seat and Table Reservation System
A system design question asking for a restaurant seat reservation system in which diners search availability by party size and time, then book, change or cancel. It tests the table-and-time inventory model, preventing double booking under concurrent requests, holds, availability caching and partitioning by restaurant.
System DesignTechnical Screen - Together AIHardSoftware EngineerDesign a GPU-aware pod scheduler
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design a GPU-aware pod scheduler states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - TripStackHardSoftware EngineerExplain Go concurrency and database choices
This question evaluates a candidate's understanding of Go concurrency primitives and scheduler behavior, cross-language concurrency comparisons, database model trade-offs (relational, document, columnar, key–value), and end-to-end storage design for high-concurrency systems.
System DesignTechnical Screen - TruefoundryMediumSoftware EngineerDesign Interviewer Availability and Atomic Booking APIs
Design a relational data model and concurrent APIs for managing interviewer availability and atomic candidate interview booking.
System DesignOnsiteConcurrencyDatabase DesignAPI Design - TuroMediumSoftware EngineerDesign trip booking REST API and schema
This question evaluates RESTful API design, temporal data modeling, inventory management strategies, request/response/status-code design, data schema and indexing, and handling booking lifecycle changes such as extensions.
System DesignTechnical Screen - TuroMediumSoftware EngineerPlan a major database version upgrade
This question evaluates competency in database upgrade planning, including data migration strategies, cutover coordination, compatibility considerations, change-data-capture versus dual-write trade-offs, and rollback and risk management.
System DesignTechnical Screen - TuroMediumSoftware EngineerRoute HTTP requests to pods in a cluster
This question evaluates understanding of request routing, service discovery, virtual service abstractions, load balancing algorithms, health checks, and fault-tolerance mechanisms within containerized cluster environments, testing competencies in distributed systems and networked service architecture.
System DesignTechnical Screen - Two SigmaMediumSoftware EngineerDesign and Scale Quantitative Optimization Workflows
Design a scalable, deterministic quantitative optimization and backtesting platform capable of executing 30,000 portfolio optimization runs with strict point-in-time data correctness and full data lineage.
System DesignTechnical ScreenDistributed SystemsQuantitative FinanceBatch Processing - UberHardSoftware EngineerCompare Paxos and Raft
This question evaluates understanding of distributed consensus protocols, replicated logs, linearizability, fault tolerance, and operational concerns such as leader election, quorum semantics, log replication, membership changes, and failure recovery.
System DesignOnsite - UberMediumSoftware EngineerDesign A URL Shortener
This question tests your ability to design a high-scale, distributed URL shortening service that handles billions of requests while ensuring low latency and data durability.
System DesignOnsiteDistributed SystemsCachingScalability - UberHardSoftware EngineerDesign Distributed Storage for Large Structured Data
Design distributed storage with versioned metadata, block replicas, layered caches, failure repair, locality-aware jobs, and resource-aware load balancing.
System DesignOnsite - UberHardSoftware EngineerDesign Food Delivery Cart
This question tests the ability to design a distributed, highly concurrent shopping cart service for a food delivery platform, focusing on transactional integrity, race conditions, and eventual consistency.
System DesignOnsiteConcurrencyDistributed SystemsCaching - UberMediumSoftware EngineerDesign MapReduce for schedule aggregation
Design MapReduce for schedule aggregation evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - UberHardSoftware EngineerDesign MapReduce for schedules
This question evaluates proficiency in scalable stream and batch data processing, windowed aggregation, event-time semantics, deduplication, skew mitigation, and distributed computation using MapReduce/Spark paradigms; it falls under System Design and Big Data/stream-processing and is commonly asked to assess designing correct, high-throughput windowed aggregations and handling late or duplicated events at scale. The level of abstraction is practical application with architectural and operational considerations rather than low-level coding, and this English summary frames the competency and interview focus for search engines.
System DesignOnsite - UberMediumSoftware EngineerDesign Product Page View Tracking
This question evaluates a candidate's ability to design scalable, reliable event-collection and analytics systems, covering client- and server-side instrumentation, event schemas/APIs, counting total versus unique views, handling high write volumes for popular products, storage choices for raw events and aggregated counters, and operational concerns such as bot filtering, deduplication, privacy, fraud prevention, freshness, reliability, and monitoring. It is commonly asked in the System Design domain to assess architectural thinking and operational trade-offs, testing both conceptual understanding of distributed systems and practical application of scalable data pipelines, storage, and observability.
System DesignOnsite - UberMediumSoftware EngineerDesign Stock Price Alerts
This question evaluates a candidate's ability to design scalable, low-latency alerting and notification systems, covering system architecture, APIs, data modeling, event-driven alert evaluation, deduplication, and multi-channel delivery; it is in the System Design category and assesses practical application skills.
System DesignTechnical Screen - UberHardSoftware EngineerDesign Uber Eats-style search function
An Uber Software Engineer system design onsite question: design the search function for an Uber Eats-style food delivery platform, covering APIs, data ingestion, geo-aware indexing and retrieval, multi-stage ranking, real-time availability updates, scaling, caching, fault tolerance, monitoring, and freshness-vs-latency trade-offs. It tests the ability to build a low-latency, location-aware, highly available search system with strong observability.
System DesignOnsite - UberMediumSoftware EngineerDesign a Distributed Logging System
This problem evaluates the ability to design a high-throughput, fault-tolerant logging infrastructure capable of ingesting and storing massive volumes of telemetry data from diverse distributed services.
System DesignOnsiteDistributed SystemsScalabilityData Pipeline
Tesla
TikTok
Together AI
Turo
Two Sigma
Uber