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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
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- 11
- Topics
- 8
- Languages
Showing 991–1020 of 9403 results
- AnthropicHardSoftware EngineerCompute a Global Mode Across Distributed Partitions
Compute an exact distributed mode with local frequency combining, key-based reducers, global winner selection, communication accounting, and skew-aware recovery.
Software Engineering FundamentalsOnsite - AnthropicHardSoftware EngineerCompute an Exact Distributed Median with Limited Communication
Compute an exact distributed median through global rank selection and count reductions, accounting for integer range, local search work, communication rounds, and even-size semantics.
Software Engineering FundamentalsOnsite - AnthropicMediumSoftware EngineerCompute exclusive times and call stack from logs
This question evaluates a candidate's ability to process single-threaded execution logs and compute per-function exclusive durations while reconstructing the call stack at a given timestamp, testing stack simulation, interval reasoning, timestamp tie-handling, parsing of time-stamped events, and algorithmic complexity analysis.
Coding & AlgorithmsTechnical Screen - AnthropicMediumSoftware EngineerCompute matrix trace and support updates
This question evaluates proficiency in matrix manipulation, incremental updates, and complexity analysis, specifically maintaining main-diagonal and anti-diagonal sums under point updates.
Coding & AlgorithmsTechnical Screen - AnthropicHardSoftware EngineerConvert Sampled Call Stacks into Start and End Events
Convert sampled call stacks to inferred start/end events using ordered common prefixes, recursive frames, explicit final-open behavior, and last-N history limitations.
Coding & AlgorithmsOnsite - AnthropicMediumSoftware EngineerConvert Sampled Call Stacks into Timestamped Function Start and End Events
Convert a sampling profiler's timestamped call stacks into function start and end events for a trace viewer, explain why ends are emitted innermost first and starts outermost first, then redesign the converter for an unbounded stream. It tests stack diffing, recursion handling and streaming state.
Software Engineering FundamentalsOnsite - AnthropicMediumMachine Learning EngineerConvert Samples into Event Intervals
This problem tests your ability to process sequential data, apply run-length encoding concepts, and implement filtering logic based on temporal constraints.
Coding & AlgorithmsTechnical ScreenArrayIntervalsData Processing - AnthropicHardSoftware EngineerConvert Stack Samples into Trace Events
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.
Coding & AlgorithmsOnsiteArraysStackStrings - AnthropicMediumMachine Learning EngineerConvert State Stream to Events
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.
Coding & AlgorithmsOnsiteArraysData ProcessingAlgorithms - AnthropicHardSoftware EngineerConvert Streaming Call-Stack Samples into Function Events
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.
Coding & AlgorithmsTechnical Screenstackalgorithmsprofiling - AnthropicMediumMachine Learning EngineerConvert stack samples to execution trace
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.
Coding & AlgorithmsTechnical Screen - AnthropicHardSoftware EngineerCrawl Same-Domain Links
This question evaluates understanding of web crawling and link traversal algorithms, including URL normalization, same-domain filtering, deduplication, and cycle-safe graph traversal.
Coding & AlgorithmsTechnical Screen - AnthropicHardSoftware EngineerDebug Python LRU Cache-Key Construction
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.
Software Engineering FundamentalsOnsitepythonconcurrencycaching - 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 - AnthropicHardMachine Learning EngineerDebug a NumPy Extremely Randomized Trees Classifier
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.
Machine LearningOnline AssessmentAnthropic FellowsAI Safety Fellowshipdebugging - AnthropicHardMachine Learning EngineerDemonstrate culture fit and leadership
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.
Behavioral & LeadershipOnsite - AnthropicHardMachine Learning EngineerDeploy a Large Model to GPU Workers
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.
System DesignOnsiteML InfrastructureDistributed SystemsNetworking - AnthropicMediumData ScientistDescribe a New Measurement That Changed a Business Decision
Prepare a behavioral answer about creating a business metric, validating its definition, and demonstrating decision impact.
Behavioral & LeadershipHR 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 - AnthropicMediumMachine Learning EngineerDescribe communication to resolve ambiguity
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.
Behavioral & LeadershipTechnical Screen - 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
Anthropic