Anthropic interview questions

Showing 91–120 of 225 results

• Design a scalable MapReduce pipelineHard

  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.

  AnthropicMachine Learning EngineerSystem DesignTechnical Screen
• Design a scalable network I/O serviceHard

  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.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Design a scalable service and model performanceHard

  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.

  AnthropicMachine Learning EngineerSystem DesignOnsite
• Design a scalable, reliable systemHard

  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.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Design a single- and multi-threaded web crawlerMedium

  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.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design an LLM Prompt Playground: Schema, Large Prompts, Sharing and Tiny EditsHard

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Design an LLM Request Batcher When Every Batch Costs a Fixed TimeHard

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Design an LLM-based binary classifierMedium

  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.

  AnthropicSoftware EngineerML System DesignTechnical Screen
• Design an inference routing and scheduling layerHard

  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.

  AnthropicMachine Learning EngineerML System DesignOnsite
• Design and implement a Python solutionMedium

  This question evaluates Python programming proficiency, algorithm design, data structure selection and justification, modular coding practices, unit testing, and time/space complexity analysis.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Design distributed median and modeHard

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Design file deduplication across nested directoriesMedium

  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.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design file deduplication at scaleMedium

  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.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Design guardrails and fallback for LLM reliabilityHard

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Design high-throughput hashing for kernelsMedium

  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.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Design production-ready dedup serviceHard

  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.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Design stack with O(1) minimum queryHard

  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.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Detect duplicate files by contentMedium

  This question evaluates filesystem traversal, efficient I/O and memory use, hashing strategies for content comparison, duplicate-detection algorithms, and time/space complexity analysis.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Detect n-length consecutive sequencesMedium

  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.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Detect runs and answer suffix queriesMedium

  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.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Discuss Ethical Judgment and Unwanted WorkMedium

  Tests a candidate's self-awareness, ethical responsibility, and ability to navigate professional disagreements or undesirable tasks while maintaining team trust and high standards.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Discuss Leadership, Motivation, and AI Safety ValuesMedium

  Tests a candidate's technical leadership, ethical decision-making during trade-off conflicts, and genuine motivation for working on responsible AI systems.

  AnthropicSoftware EngineerBehavioral & LeadershipHR Screen
• Discuss culture and mission alignmentMedium

  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.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Distribute 500 GB of Model Weights to 1,000 GPU Workers: Lower Bound and DesignMedium

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Estimate FFN Compute, Memory, and Sharding CommunicationHard

  Estimate standard FFN FLOPs, parameter and activation bytes, and sharding communication, then compare compute, HBM, and network limits with explicit layout assumptions.

  AnthropicSoftware EngineerML System DesignOnsite
• Estimate VRAM and compare model parallelismHard

  This question tests the ability to calculate memory overhead for transformer model parameters and activation buffers, while evaluating the architectural trade-offs between tensor and pipeline parallelism for large-scale training.

  AnthropicSoftware EngineerML System DesignOnsite
• Explain AI Safety and Weigh Advanced AI Benefits and RisksHard

  Tests a candidate's nuanced understanding of AI safety, their ability to weigh catastrophic and near-term risks against transformational benefits, and their alignment with safety-first engineering principles.

  AnthropicSoftware EngineerBehavioral & LeadershipHR Screen
• Explain Anthropic motivation and leadership storiesMedium

  This question evaluates leadership, ethical reasoning, cultural fit, communication, and project ownership, targeting Behavioral & Leadership competencies for a software engineer role.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Explain CPU-Bound vs I/O-Bound WorkHard

  This question evaluates understanding of CPU-bound versus I/O-bound workloads and their interaction with concurrency models such as multithreading, asynchronous I/O, and multiprocessing in Python, including the impact of the Global Interpreter Lock (GIL) and relevance to tasks like web crawling, parsing, and heavy computation.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Explain How You Know a Professional ReferrerMedium

  This question assesses your ability to articulate the professional context and scope of a workplace referral, distinguishing genuine technical collaboration from casual networking while reinforcing your independent qualifications.

  AnthropicSoftware EngineerBehavioral & LeadershipHR Screen