Anthropic interview questions

Showing 31–60 of 225 results

• Convert Samples into Event IntervalsMedium

  This problem tests your ability to process sequential data, apply run-length encoding concepts, and implement filtering logic based on temporal constraints.

  AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Convert Stack Samples into Trace EventsHard

  This problem tests your ability to manipulate tree-like path structures and manage state transitions using a stack, specifically focusing on efficiently calculating diffs between consecutive call stacks.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Convert State Stream to EventsMedium

  This question tests the ability to process a stream of state changes, group consecutive identical values into distinct event intervals, and filter these events based on duration.

  AnthropicMachine Learning EngineerCoding & AlgorithmsOnsite
• Convert Streaming Call-Stack Samples into Function EventsHard

  Tests the ability to process discrete call-stack snapshots into properly nested, ordered function start and end events using longest common prefix analysis and stack unwinding.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Convert stack samples to execution traceMedium

  This question evaluates understanding of sampled stack traces, event reconstruction, handling ambiguous or incomplete profiling data, and algorithmic reasoning about correctness and time/space complexity.

  AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Crawl Same-Domain LinksHard

  This question evaluates understanding of web crawling and link traversal algorithms, including URL normalization, same-domain filtering, deduplication, and cycle-safe graph traversal.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Debug Python LRU Cache-Key ConstructionHard

  Implement and debug a thread-safe, bounded LRU memoization decorator that generates canonical cache keys across equivalent argument patterns, distinguishes conflicting types, and avoids caching exceptions.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsOnsite
• Debug Tokenization and DetokenizationHard

  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.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsOnsite
• Debug a GRPO training loop and explain ratiosMedium

  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.

  AnthropicSoftware EngineerMachine LearningTechnical Screen
• Debug a NumPy Extremely Randomized Trees ClassifierHard

  Anthropic Fellows debugging-OA practice: find the eight crash and accuracy bugs in a from-scratch NumPy Extra-Trees classifier until it matches scikit-learn.

  AnthropicMachine Learning EngineerMachine LearningOnline Assessment
• Demonstrate culture fit and leadershipHard

  This question evaluates interpersonal and leadership competencies for a Machine Learning Engineer role, including cultural adaptability, trust-building, stakeholder alignment, cross-cultural communication, inclusion, delivering and receiving difficult feedback, and validating decisions with guardrails or safety checks.

  AnthropicMachine Learning EngineerBehavioral & LeadershipOnsite
• Deploy a Large Model to GPU WorkersHard

  Design a high-throughput, fault-tolerant artifact distribution system to deploy a 500 GB large language model across thousands of GPU worker nodes under strict time and bandwidth constraints.

  AnthropicMachine Learning EngineerSystem DesignOnsite
• Describe a New Measurement That Changed a Business DecisionMedium

  Prepare a behavioral answer about creating a business metric, validating its definition, and demonstrating decision impact.

  AnthropicData ScientistBehavioral & LeadershipHR Screen
• Describe a Python design-and-coding approach in ColabMedium

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Describe a Strongly Held View That Proved WrongMedium

  Tests intellectual humility, technical judgment, and the ability to pivot based on disconfirming evidence while taking full accountability for downstream impacts.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Describe communication to resolve ambiguityMedium

  Describe communication to resolve ambiguity evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes 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 EngineerBehavioral & LeadershipTechnical Screen
• Describe failure impact and resolve cross-functional conflictHard

  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.

  AnthropicSoftware EngineerBehavioral & LeadershipTechnical Screen
• Describe handling AI safety concernsMedium

  This question evaluates a candidate's competency in AI safety risk management, including identification, assessment, mitigation, monitoring, and cross‑functional leadership within software engineering.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Describe your most impactful projectMedium

  This question evaluates a candidate's leadership, ownership, communication, and technical judgment by probing their most impactful project and personal contributions.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Design Distributed Sorting Around an Explicit Helper ContractMedium

  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.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsOnsite
• Design Durable One-to-One Web Messaging at Large ScaleHard

  Design Web-only one-to-one messaging at 100 million DAU with durable deduplication, chat sharding, membership indexes, offline recovery, and replica-aware reads.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Design GPU inference request batchingMedium

  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.

  AnthropicSoftware EngineerML System DesignOnsite
• Design Instagram (Feed, Photos, and Friend Recommendations)Medium

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Design Large Model-Weight Distribution to GPU WorkersHard

  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.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Design Model Weight DistributionMedium

  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.

  AnthropicSoftware EngineerML System DesignOnsite
• Design One-to-One ChatMedium

  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.

  AnthropicSoftware EngineerSystem DesignOnsite
• Design Peer-to-Peer Model Distribution Under a Shared Link CapMedium

  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.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Design Safe Distribution and Activation of Model WeightsMedium

  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.

  AnthropicSoftware EngineerML System DesignOnsite
• Design Telemetry Ingestion and Reconcile Metric NamesMedium

  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.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Design a Batched LLM Inference Service Assuming Each Batch Takes 100 msHard

  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.

  AnthropicSoftware EngineerSystem DesignOnsite