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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 1051–1080 of 9403 results
- 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 - AnthropicMediumSoftware EngineerDesign a one-to-one chat system
This problem tests your ability to design a scalable, real-time one-to-one messaging system by addressing requirements for message ordering, delivery guarantees, and user presence.
System DesignTechnical ScreenWebSocketsMessagingScalability - 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 - AnthropicMediumSoftware EngineerDesign a prompt-sharing platform
This question assesses the ability to design a scalable content-sharing platform, focusing on data modeling, efficient retrieval of user-generated prompts, and the handling of concurrent access and permissions.
System DesignTechnical ScreenAPI DesignData ModelingScalability - AnthropicHardMachine Learning EngineerDesign a scalable MapReduce pipeline
Design a scalable MapReduce pipeline 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 - AnthropicHardSoftware EngineerDesign a scalable network I/O service
This question evaluates a candidate's ability to design a scalable, highly available network I/O backend, testing competencies in distributed systems, storage architecture, CDN and caching integration, API and data model design, capacity planning, failure handling, observability, and security.
System DesignTechnical Screen - 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 - AnthropicHardSoftware EngineerDesign a scalable, reliable system
The prompt evaluates competence in large-scale distributed system design, covering storage architecture, consistency and replication strategies, caching, APIs and data models, operational reliability, security, and cost/capacity planning.
System DesignTechnical Screen - AnthropicMediumSoftware EngineerDesign a single- and multi-threaded web crawler
This question evaluates skills in web crawling, URL parsing and fragment sanitization, graph traversal for deduplication, and concurrent programming for thread-safe crawling, and it falls under the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AnthropicHardSoftware EngineerDesign an LLM Prompt Playground: Schema, Large Prompts, Sharing and Tiny Edits
A system design interview to build an LLM prompt playground where users write, run, save and share prompts, starting from the database schema. Follow-ups probe storing very large prompts, designing sharing, and handling frequent one- or two-character edits to a large prompt without wasting storage.
System DesignOnsite - AnthropicHardSoftware EngineerDesign an LLM Request Batcher When Every Batch Costs a Fixed Time
Design the serving path for a code-completion model whose accelerators run requests in batches. The interviewer says one batch always takes a fixed 250 ms. Tests batching policy, capacity math, latency budgets, routing results back to callers, failure handling, and questioning the fixed-latency assumption.
System DesignOnsitellm-servingbatchingqueueing - AnthropicMediumSoftware EngineerDesign an LLM-based binary classifier
This question evaluates knowledge of LLM-based binary classification, probabilistic scoring and numerical stability (including log-prob handling and log-sum-exp), prompt construction, batching and sequence-level aggregation, calibration and evaluation metrics, plus analysis of failure modes and inference-time complexity.
ML System DesignTechnical Screen - AnthropicHardMachine Learning EngineerDesign an inference routing and scheduling layer
This question evaluates system-design and production machine learning engineering skills, specifically distributed systems, routing and scheduling, multi-tenant isolation, caching, dynamic batching, and capacity planning for heterogeneous GPU/CPU inference backends.
ML System DesignOnsite - AnthropicMediumSoftware EngineerDesign and implement a Python solution
This question evaluates Python programming proficiency, algorithm design, data structure selection and justification, modular coding practices, unit testing, and time/space complexity analysis.
Coding & AlgorithmsOnsite - AnthropicHardSoftware EngineerDesign distributed median and mode
This question evaluates skills in distributed systems and large-scale analytics, covering data partitioning, streaming and sliding-window processing, quantile and heavy-hitter summarization, fault tolerance, and trade-off analysis for time, space, and network I/O.
System DesignOnsite - AnthropicMediumSoftware EngineerDesign file deduplication across nested directories
This question evaluates a candidate's competency in file system traversal, robust I/O handling including symbolic links and cycles, content-based deduplication, and algorithmic efficiency within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AnthropicMediumSoftware EngineerDesign file deduplication at scale
This question evaluates understanding of file-system algorithms, hashing and deduplication techniques, scalable I/O and memory management, and correctness considerations for hash collisions, and it belongs to the Coding & Algorithms domain.
Coding & AlgorithmsOnsite - AnthropicHardSoftware EngineerDesign guardrails and fallback for LLM reliability
Design an end-to-end safety and reliability middleware layer for a production LLM service, focusing on low-latency input/output guardrails, early streaming interception, multi-tier fallback strategies, and observability.
System DesignOnsiteLLMSafetyStreaming Architecture - AnthropicMediumSoftware EngineerDesign high-throughput hashing for kernels
This question evaluates understanding of cache-efficient, high-throughput hash lookup design and related competencies such as hashing strategies, probe behavior, memory layout, vectorization, bitwise optimizations, prefetching, alignment, and microarchitectural performance trade-offs.
Coding & AlgorithmsOnsite - AnthropicHardSoftware EngineerDesign production-ready dedup service
This question evaluates system design and distributed storage competencies, focusing on scalable file deduplication, content-defined chunking, metadata and index design, multi-tenant architectures, and operational reliability across regions.
System DesignTechnical Screen - AnthropicHardSoftware EngineerDesign stack with O(1) minimum query
This problem tests your ability to augment a standard stack with metadata to perform efficient minimum queries without sacrificing the constant-time complexity of stack operations.
Coding & AlgorithmsTechnical ScreenStackData StructuresAlgorithms - AnthropicMediumSoftware EngineerDetect duplicate files by content
This question evaluates filesystem traversal, efficient I/O and memory use, hashing strategies for content comparison, duplicate-detection algorithms, and time/space complexity analysis.
Coding & AlgorithmsOnsite - AnthropicMediumSoftware EngineerDetect n-length consecutive sequences
This question evaluates a candidate's competency in algorithm design and data-structure reasoning for detecting n-length consecutive integer sequences, encompassing handling of duplicates, negative values, and dynamic insertions and deletions.
Coding & AlgorithmsTechnical Screen - AnthropicMediumSoftware EngineerDetect runs and answer suffix queries
This question evaluates proficiency in array processing, run-length detection, algorithmic complexity analysis, and the use of range/suffix data structures within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AnthropicMediumSoftware EngineerDiscuss Ethical Judgment and Unwanted Work
Tests a candidate's self-awareness, ethical responsibility, and ability to navigate professional disagreements or undesirable tasks while maintaining team trust and high standards.
Behavioral & LeadershipOnsitebehavioralethicsleadership - AnthropicMediumSoftware EngineerDiscuss Leadership, Motivation, and AI Safety Values
Tests a candidate's technical leadership, ethical decision-making during trade-off conflicts, and genuine motivation for working on responsible AI systems.
Behavioral & LeadershipHR ScreenBehavioralLeadershipAI Safety - AnthropicMediumSoftware EngineerDiscuss culture and mission alignment
This question evaluates alignment with organizational mission, ethical and safety judgment, decision-making under ambiguity, receptiveness to blunt feedback, collaboration style, and practices for sustaining engineering quality—core behavioral and leadership competencies for software engineers.
Behavioral & LeadershipOnsite - AnthropicMediumSoftware EngineerDistribute 500 GB of Model Weights to 1,000 GPU Workers: Lower Bound and Design
Copy 500 GB of model weights to 100 to 1,000 GPU workers whose 10 Gbps links are shared between upload and download. Derive the lower bound on distribution time, evolve a naive fan-out into a chunked peer-to-peer design, and handle stragglers, failures and cutover. It tests bandwidth reasoning and large-scale distribution design.
System DesignOnsite
Anthropic