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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
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- 11
- Topics
- 8
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Showing 1021–1050 of 9403 results
- AnthropicMediumSoftware EngineerDesign Model Weight Distribution
This question evaluates a candidate's competency in large-scale ML system design and distributed systems, focusing on weight file distribution, versioning and rollout strategies, consistency models, integrity verification, access control, rollback mechanisms, and operational scalability.
ML System DesignOnsite - AnthropicMediumSoftware EngineerDesign One-to-One Chat
This question evaluates expertise in designing scalable, real-time one-to-one messaging systems, testing knowledge of distributed systems principles, message persistence and retrieval, delivery semantics, session and WebSocket management, ordering, retries, deduplication, and trade-offs between streaming and in-memory technologies such as Kafka and Redis. It is commonly asked to assess architectural reasoning about latency, availability, ordering guarantees and reliability within the System Design domain, and requires a mix of conceptual architectural understanding and practical implementation-level considerations.
System DesignOnsite - AnthropicMediumSoftware EngineerDesign Peer-to-Peer Model Distribution Under a Shared Link Cap
Design an internal peer-to-peer artifact distribution system to deliver large model weights from a single seed machine to thousands of cluster workers subject to a shared aggregate upload-and-download network link limit.
System DesignTechnical Screensystem-designdistributed-systemsnetworking - AnthropicMediumSoftware EngineerDesign Safe Distribution and Activation of Model Weights
This problem tests the ability to architect a robust, secure, and atomic deployment system for large binary artifacts (model weights) across a distributed inference cluster.
ML System DesignOnsiteDistributed SystemsMLOpsSystem Design - AnthropicMediumSoftware EngineerDesign Telemetry Ingestion and Reconcile Metric Names
Design a scalable telemetry ingestion pipeline that standardizes and reconciles metric naming inconsistencies and schema drift across disparate client libraries, historical stores, and real-time query engines.
System DesignTechnical ScreenData InfrastructureTelemetryStream Processing - AnthropicHardSoftware EngineerDesign a Batched LLM Inference Service Assuming Each Batch Takes 100 ms
A system design question on serving large language model inference by batching requests, given the interviewer's assumption that every batch finishes in 100 ms. It tests batching policy, capacity math, latency budgets, returning results to callers, failure handling, and whether a fixed batch time holds when prompt and response lengths vary.
System DesignOnsite - AnthropicMediumSoftware EngineerDesign a Collaborative Prompt Playground
Design a collaborative prompt playground with conflict-aware editing, immutable versions, asynchronous model runs, and reliable input and result provenance.
System DesignOnsite - AnthropicHardSoftware EngineerDesign a Concurrent Image Processing Service
Design a resilient, distributed image-processing pipeline that safely scales from a single worker to hundreds of concurrent nodes while guaranteeing job lease safety, idempotent output writes, and clean cancellation.
System DesignOnsitesystem-designdistributed-systemsconcurrency - AnthropicHardSoftware EngineerDesign a Crash-Resilient LRU Cache
This question evaluates a candidate's competency in designing a crash-resilient LRU cache, including which data and ordering must be persisted, durability and recovery concerns, and trade-offs between latency and persistence.
System DesignTechnical Screen - AnthropicMediumSoftware EngineerDesign a Distributed Rate Limiter
Tests the candidate's ability to design a low-latency, highly available distributed rate-limiting service that coordinates shared state across geographically distributed API gateways while gracefully handling network partitions and datastore failures.
System DesignOnsiteDistributed SystemsRate LimitingRedis - AnthropicMediumMachine Learning EngineerDesign a Double Descent Experiment
Tests your ability to design a controlled synthetic experiment demonstrating double descent, explain the mathematical causes of test error divergence near the interpolation threshold, and analyze the interplay between noise, conditioning, and regularization.
Machine LearningHR Screendouble-descentgeneralizationlinear-models - AnthropicHardMachine Learning EngineerDesign a Dynamically Batched Inference API
Design a scalable, multi-tenant LLM inference serving platform that balances Time-to-First-Token (TTFT) and throughput using dynamic iteration-level batching, KV cache memory management, and strict admission control.
System DesignOnsiteLLM ServingDistributed SystemsInference Engine - AnthropicHardSoftware EngineerDesign a GPU inference API
This Anthropic ML system design onsite asks you to design a multi-tenant, GPU-backed inference API that serves many models and versions -- including large autoregressive LLMs -- under explicit latency SLOs. It spans API and schema design, streaming and async jobs, continuous batching with prefill/decode separation, KV-cache and GPU memory management, multi-model hosting on shared GPUs, heterogeneous autoscaling and placement, multi-region routing, canary rollout, and reliability and cost controls. The signature probe is the diagnostic scenario where CPU utilization is low but the GPUs are saturated.
ML System DesignOnsite - AnthropicMediumSoftware EngineerDesign a Low-Latency GPU Inference Service
This problem tests the ability to design a high-throughput, low-latency inference architecture for large language models, focusing on hardware utilization and bottleneck mitigation.
ML System DesignTechnical ScreenMLOpsLLMDistributed Systems - AnthropicMediumSoftware EngineerDesign a Model Inference API and Optimize Its GPU Usage
A system design interview question that asks you to design an inference API for a trained model served on GPUs, covering the public API, streamed responses, request queueing, scheduling onto GPU workers, overload handling and failure recovery. Follow-up questions probe how to raise GPU throughput and memory efficiency without missing latency targets.
System DesignOnsite - AnthropicMediumSoftware EngineerDesign a One-on-One Chat Service
Design a highly available, low-latency one-on-one messaging system capable of delivering messages in real time, maintaining strict ordering, and handling offline synchronization.
System DesignTechnical Screensystem-designdistributed-systemsreal-time - AnthropicMediumSoftware EngineerDesign a Parallel Image Processor
This problem tests your ability to design a thread-safe, parallelized image processing framework that handles neighborhood-based pixel operations without race conditions.
Software Engineering FundamentalsTechnical ScreenConcurrencyMultithreadingImage Processing - AnthropicMediumSoftware EngineerDesign a Parallel and Resumable Model Downloader
Design a parallel model downloader with version-bound byte ranges, resumable verified pieces, bounded resource use, and atomic publication of complete artifacts.
System DesignOnsite - AnthropicMediumSoftware EngineerDesign a Resilient Chat System
Design a resilient, distributed 1:1 and group messaging system focusing on durability guarantees, strict message ordering, robust fan-out, and reliable offline catch-up under partial failures.
System DesignTechnical Screensystem-designdistributed-systemschat-architecture - AnthropicMediumSoftware EngineerDesign a Resumable Parallel Downloader for Game Client Patches
Design a game launcher's patch downloader that fetches a multi-gigabyte update in parallel byte-range chunks, survives crashes and connection drops without re-downloading verified bytes, and only ever exposes a complete, verified patch to the game client.
System DesignOnsitedistributed-systemsresumabilityintegrity - AnthropicMediumSoftware EngineerDesign a Resumable, Parallel Fetcher for Huge Dataset Releases
Design a node-side fetcher that downloads multi-terabyte dataset releases in parallel byte ranges, resumes from verified pieces after crashes, and atomically publishes only complete releases. Tests manifest design, range-request validation, durable progress tracking, and atomic filesystem publication.
System DesignOnsitesystem-designdistributed-systemsstorage - AnthropicMediumSoftware EngineerDesign a Shared API Request Workbench With Versioned Runs
Design a team tool where engineers co-edit a shared collection of HTTP request templates, freeze them into immutable versions, and execute versions asynchronously against third-party APIs with full provenance of inputs, config, and results.
System DesignOnsitesystem-designcollaborationversioning - AnthropicMediumSoftware EngineerDesign a Team SQL Workbench with Snapshots and Async Runs
Design a multi-user SQL workbench where analysts co-edit drafts, freeze immutable snapshots, and launch long-running warehouse queries whose results stay traceable to their exact inputs. Tests concurrency control, immutability boundaries, and reliable async execution.
System DesignOnsitecollaborationversioningasync-jobs - AnthropicMediumSoftware EngineerDesign a Telemetry Pipeline That Handles Inconsistent Metric Names from Old Clients
Design a telemetry pipeline that ingests metrics from a large fleet of deployed clients, then handle legacy clients that cannot be updated and report the same metric under inconsistent names. It tests durable ingestion, stream processing and storage design, canonical metric catalogs, version-scoped name mapping and schema evolution.
System DesignOnsite - AnthropicHardSoftware EngineerDesign a Translation API That Batches Requests on a Fixed Clock
Design a hosted translation API where a GPU worker runs one batch of pending requests every fixed round, and the candidate must derive throughput, batch-closing policy, and failure handling from that one constraint.
System DesignOnsitesystem-designbatchingcapacity-planning - AnthropicHardSoftware EngineerDesign a batch inference API
This question evaluates a candidate's ability to design asynchronous batch inference APIs and systems, including API schema and job lifecycle design, idempotency semantics, queueing and worker scaling, batching and accelerator utilization, rate limiting, observability, and error handling.
ML System DesignOnsite - AnthropicHardSoftware EngineerDesign a concurrent web crawler
An Anthropic system design question: design and implement a concurrent web crawler, from a single-threaded baseline to manual threads, a thread pool and asyncio. It covers the URL frontier, deduplication across workers and redirects, robots.txt and per-host politeness, Retry-After backoff, completion detection, memory sizing, testing and monitoring.
System DesignTechnical Screen - AnthropicMediumSoftware EngineerDesign a distributed web crawler
This question tests the ability to design a scalable, fault-tolerant distributed web crawler that handles URL management, distributed coordination, and domain politeness.
System DesignTechnical ScreenDistributed SystemsScalabilityCaching - AnthropicHardSoftware EngineerDesign a high-concurrency LLM inference service
This problem evaluates your ability to design a scalable LLM inference serving platform, focusing on throughput optimization, memory constraints, and scheduling strategies for long-running inference requests.
ML System DesignOnsiteLLMInferenceGPU - AnthropicHardSoftware EngineerDesign a low-latency ML inference API
This question evaluates competency in ML system design for real-time, low-latency inference APIs, including multitenancy, SLO/SLI definition, feature retrieval, model serving and rollout strategies, observability, cost control, and security/compliance within the ML System Design category.
ML System DesignOnsite
Anthropic