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Software Engineer 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
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Showing 661–690 of 5874 results
- AnthropicHardSoftware EngineerDebug Tokenization and Detokenization
Implement and debug a prefix-based tokenizer and detokenizer class, ensuring deterministic longest-match greedy tokenization, strict vocabulary validation, explicit error handling for unencodable characters, and guaranteed round-trip fidelity.
Software Engineering FundamentalsOnsitestring-algorithmstriesoftware-engineering - AnthropicMediumSoftware EngineerDebug a GRPO training loop and explain ratios
This question evaluates debugging and implementation knowledge for on-policy reinforcement learning, focusing on GRPO/PPO-style training loops, importance-sampling ratios, log-prob computations, masking, and advantage normalization.
Machine LearningTechnical Screen - AnthropicMediumSoftware EngineerDescribe a Python design-and-coding approach in Colab
This question evaluates a candidate's ability to design and implement a Python-based solution within a cloud-hosted notebook environment, covering requirements elicitation, component and data-structure selection, modularization, testing, dependency and environment management, and basic performance analysis.
System DesignOnsite - AnthropicMediumSoftware EngineerDescribe a Strongly Held View That Proved Wrong
Tests intellectual humility, technical judgment, and the ability to pivot based on disconfirming evidence while taking full accountability for downstream impacts.
Behavioral & LeadershipOnsiteBehavioralIntellectual HumilityEngineering Judgment - AnthropicHardSoftware EngineerDescribe failure impact and resolve cross-functional conflict
This question evaluates a software engineer's behavioral and leadership competencies, focusing on accountability for failed projects, impact recovery, conflict diagnosis, stakeholder management, and cross-functional collaboration.
Behavioral & LeadershipTechnical Screen - 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 - AnthropicHardSoftware EngineerDesign Durable One-to-One Web Messaging at Large Scale
Design Web-only one-to-one messaging at 100 million DAU with durable deduplication, chat sharding, membership indexes, offline recovery, and replica-aware reads.
System DesignTechnical Screen - 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 - AnthropicHardSoftware EngineerDesign Large Model-Weight Distribution to GPU Workers
Design a high-throughput, fault-tolerant distribution platform that transfers multi-hundred-gigabyte model weights to tens of thousands of GPU worker nodes with integrity validation, progressive rollouts, and fast rollback capabilities.
System DesignTechnical Screensystem-designml-infrastructuredistributed-systems - 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 - 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
Anthropic