Anthropic interview questions

Showing 151–180 of 225 results

• How do you lead under risk and uncertainty?Hard

  This question evaluates leadership competencies—risk assessment, stakeholder management, conflict resolution, and decision-making under uncertainty in engineering contexts.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• How do you review a design document?Hard

  This question assesses a candidate's ability to critically evaluate technical design documentation by identifying architectural risks, scalability bottlenecks, and gaps in operational planning.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsOnsite
• How to Handle the First-Round Interview at an AI Safety LabMedium

  A structured prompt for the earliest conversation in the loop at a lab whose mission is AI safety, where most of the time goes to why you want to work there specifically and how you think about the risks of the technology, not resume review. Tests depth, specificity, and whether your safety views connect to real experience.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• How to stream a large file to 1000 hosts fastestMedium

  Design a high-throughput distribution system to broadcast a large file (e.g., 100 GB model checkpoint) to 1,000 cluster hosts in minimum time, analyzing network bottlenecks and replication topologies.

  AnthropicSoftware EngineerSystem DesignOnsite
• How would you scale batch image pipelines?Medium

  This problem evaluates your ability to design a distributed, fault-tolerant batch processing system for high-throughput image transformation tasks.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Identify and mitigate deduplication program risksHard

  This question evaluates understanding of system design and practical engineering competencies required for building resilient file deduplication tools, covering filesystem semantics, error handling, hashing and collision considerations, concurrency, performance bottlenecks, cross-platform interoperability, and safety trade-offs.

  AnthropicSoftware EngineerSystem DesignTechnical Screen
• Identify the Most Important AI Safety RiskMedium

  Tests your ability to analyze AI alignment failure modes—specifically sycophancy and emotional over-reliance—and formulate rigorous evaluation metrics, training mitigations, and product-level guardrails.

  AnthropicSoftware EngineerMachine LearningHR Screen
• Implement Parallel Image ProcessingMedium

  This question evaluates competency in concurrent and parallel processing, practical image manipulation with Python libraries (Pillow), resource management for memory and file I/O, robust error handling, and performance measurement.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement Persistent Memoization LRU CacheHard

  This problem tests your ability to design a decorator that memoizes function results using an LRU policy while ensuring the cache survives process restarts via disk serialization.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement Python LRU cache with args and persistenceMedium

  This question evaluates a candidate's ability to design and implement an LRU cache in Python that supports variable-length positional and keyword arguments with canonical, deterministic hashing for semantically equivalent calls and O(1) get/put via a hash map plus a doubly linked list; it falls under the Coding & Algorithms domain and primarily tests practical implementation skills, API robustness, hashing/equality subtleties, and algorithmic complexity guarantees. The persistence follow-up evaluates understanding of serialization and storage design — on-disk formats, versioning, and atomic write strategies — and compares trade-offs between binary pickle and JSON-based serialization in terms of security, compatibility, and performance, reflecting both conceptual design and practical system-level considerations.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement Python LRU cache with varargsMedium

  This question evaluates a candidate's ability to implement an LRU caching mechanism in Python, including decorator design, argument canonicalization for positional args, *args, and **kwargs, handling unhashable inputs, eviction semantics, cache metadata operations (clear, cache_info), and JSON-based persistence and versioning.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement Task Management and Duplicate DetectionMedium

  This problem tests your ability to design an in-memory task management system with concurrency-aware state tracking and implement an efficient file-deduplication routine using hashing.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement a Banking SystemMedium

  This question evaluates skills in designing and implementing a stateful in-memory banking system, covering account modeling, transfer lifecycle and expiration handling, balance management, and tracking aggregate outgoing amounts.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement a Batch Image ProcessorMedium

  Design and implement an in-memory image transformation engine that applies sequential operations (grayscale, brightness scaling, nearest-neighbor resizing) over a batch of images efficiently.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement a Duplicate-File ReporterEasy

  Implement an efficient file system scanner that identifies duplicate files while minimizing disk I/O and gracefully handling filesystem errors.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Implement a Least-Recently-Used CacheMedium

  This question evaluates a candidate's ability to design and implement an efficient in-memory cache with eviction behavior, testing understanding of data structure selection, state management, and time/space complexity under capacity constraints.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement a Longest-Match TokenizerHard

  Design and implement a greedy longest-match text tokenizer that validates vocabulary, supports configurable unknown-token coalescing, and emits token IDs alongside their exact substring matches.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement a Parallel Image ProcessorMedium

  Design and implement a concurrent batch image processor that executes a sequence of matrix transforms across worker threads or processes while isolating per-job failures.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement a Simplified DNS ResolverHard

  Implement an in-memory DNS resolver supporting A and CNAME record lookups, domain name normalization, TTL expiration, and CNAME alias chain cycle detection.

  AnthropicMachine Learning EngineerCoding & AlgorithmsOnline Assessment
• Implement a Time-Aware Task ManagerHard

  Design an in-memory task manager supporting per-user task versioning, time-to-live (TTL) expiration, and point-in-time historical queries.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Implement a hierarchical file storeHard

  This problem tests your ability to design a hierarchical file system data structure and implement operations for directory traversal, file manipulation, and path normalization.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement a web crawler using a provided APIMedium

  This problem tests your ability to design a multi-threaded web crawler that efficiently traverses a site while respecting domain constraints and avoiding redundant work.

  AnthropicMachine Learning EngineerCoding & AlgorithmsOnsite
• Implement an extensible prefix treeMedium

  This question evaluates the ability to design and implement an extensible prefix tree (trie), covering competencies in Unicode-aware string handling, memory and time optimization, concurrency or thread-safety considerations, and support for operations like insert, search, startsWith, countPrefix and erase.

  AnthropicMachine Learning EngineerCoding & AlgorithmsOnsite
• Implement an in-memory DB with TTL backup/restoreMedium

  Implement an in-memory nested key-value store supporting field-level time-to-live (TTL) expiration and snapshot backup/restore operations.

  AnthropicSoftware EngineerCoding & AlgorithmsOnline Assessment
• Implement and analyze custom attentionHard

  This question evaluates implementation and analysis skills for scaled dot-product attention, testing competencies in efficient tensorized PyTorch coding, numerical stability and mixed-precision handling, masking semantics (causal and padding), multi-head shape correctness, and unit-test validation including gradient and edge-case behavior.

  AnthropicSoftware EngineerMachine LearningOnsite
• Implement and derive backprop from scratchMedium

  This question evaluates understanding and practical implementation of neural network fundamentals, specifically analytic backpropagation and gradient derivation, numerically stable binary cross-entropy computation, parameter initialization, gradient descent updates, and gradient checking.

  AnthropicSoftware EngineerMachine LearningOnsite
• Implement cluster status trackerMedium

  This question evaluates a candidate's competence in data structures and algorithms for time-ordered state tracking, time-series indexing, conflict resolution (last-write-wins), memory-efficient storage, and concurrent system behavior under high update rates.

  AnthropicMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Implement crawler, dedup, and persistent LRUMedium

  This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Implement crawler, dedup, and persistent LRU states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement file deduplication at scaleMedium

  Implement streaming file deduplication that filters by size, fingerprint and hash, then compares bytes to rule out hash collisions. It covers large-file chunking, batched disk I/O, parallelizing across cores or machines, a dry-run report, and hard links that never cross devices. Follow-ups turn it into a command-line tool that handles symbolic links, unreadable files, error cases, and options to move or delete duplicates.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Implement hostname-restricted web crawlerMedium

  This question evaluates skills in graph traversal and cycle detection, hostname-based filtering, URL parsing, and basic network error handling. Commonly asked in Coding & Algorithms interviews, it assesses algorithmic reasoning about traversal strategy and complexity (time and space) analysis while balancing conceptual understanding with practical implementation details.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen