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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
- Companies
- 11
- Topics
- 8
- Languages
Showing 391–420 of 3640 results
- AnthropicMediumSoftware EngineerDescribe handling AI safety concerns
This question evaluates a candidate's competency in AI safety risk management, including identification, assessment, mitigation, monitoring, and cross‑functional leadership within software engineering.
Behavioral & LeadershipOnsite - AnthropicMediumSoftware EngineerDescribe your most impactful project
This question evaluates a candidate's leadership, ownership, communication, and technical judgment by probing their most impactful project and personal contributions.
Behavioral & LeadershipOnsite - AnthropicMediumSoftware EngineerDesign Distributed Sorting Around an Explicit Helper Contract
Design and implement the coordination logic for a distributed range-based sort across multiple worker nodes, specifying the worker helper contract and handling key skew and duplicate values.
Software Engineering FundamentalsOnsitedistributed-systemsalgorithmssystem-design - AnthropicMediumSoftware EngineerDesign GPU inference request batching
This question evaluates a candidate's competency in ML System Design, focusing on GPU-based inference batching, scheduling, autoscaling, and the operational concerns of routing, compatibility, and fault-tolerance.
ML System DesignOnsite - AnthropicMediumSoftware EngineerDesign Instagram (Feed, Photos, and Friend Recommendations)
Design a scalable social media platform supporting photo uploads, timeline feed generation, follow graphs, and friend recommendations. This problem tests distributed storage, feed fan-out strategies under skewed follower distributions, and graph-based recommendation systems.
System DesignOnsiteDistributed SystemsCachingFeed Generation - 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 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 - 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 - 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 - 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 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 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 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 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 - AnthropicMediumMachine Learning EngineerDesign a model downloader
This question assesses the architectural design of a distributed, secure, and resilient system capable of deploying large-scale machine learning models to heterogeneous edge devices.
ML System DesignOnsiteSystem DesignMLOpsDistributed Systems - AnthropicHardSoftware EngineerDesign a profiling plan for kernels
This question evaluates competency in performance profiling, experimental design, microarchitectural analysis, statistical methodology, and kernel-level performance engineering.
Analytics & ExperimentationOnsite - AnthropicHardSoftware EngineerDesign a prompt playground
This question evaluates a candidate's competency in large-scale system design, real-time streaming and prompt execution, multi-tenant data modeling, versioning and collaboration workflows, security and privacy controls, observability and cost tracking, and abuse prevention.
System DesignOnsite - AnthropicHardSoftware EngineerDesign a prompt processing backend
Design a prompt processing backend evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnsite - AnthropicHardMachine Learning EngineerDesign a scalable service and model performance
This question evaluates a candidate's ability in large-scale distributed system design, performance modeling, capacity planning, and operational reliability for latency-sensitive, multi-region key-value services.
System DesignOnsite
Anthropic