Anthropic interview questions

Showing 121–150 of 225 results

• Explain How You Resolved a Professional ConflictHard

  Explain a professional conflict through fair opposing views, shared criteria, a concrete resolution process, personal adjustments, and follow-through.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Explain Mission Alignment and Readiness for an Individual-Contributor RoleMedium

  Tests your ability to articulate authentic motivation for AI safety research, map your systems engineering background to safety infrastructure, and justify a deliberate transition into an individual-contributor role.

  AnthropicSoftware EngineerBehavioral & LeadershipHR Screen
• Explain Ownership of a Project Business MetricMedium

  Tests an engineer's ability to define, instrument, and own business or platform metrics, ensuring alignment with technical outcomes while actively managing guardrails and proxy risks.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Explain Why This Mission and Why Change Roles NowHard

  Tests your ability to articulate genuine alignment with AI safety and frontier model infrastructure, justify the timing of a career pivot, and link hands-on systems experience to high-scale performance engineering challenges.

  AnthropicPerformance / Infrastructure Software EngineerBehavioral & LeadershipTechnical Screen
• Explain Your AI Safety ValuesHard

  Assesses how an engineer conceptualizes AI safety and risk, translates ethical principles into concrete engineering trade-offs, and navigates organizational friction or ambiguity when escalating potential harms.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Explain Your Motivation and Practical Approach to AI SafetyHard

  Tests your ability to articulate authentic motivation for AI safety, critically engage with Anthropic's specific frameworks (like the Responsible Scaling Policy), and connect abstract risk mitigation to concrete software engineering safeguards and trade-offs.

  AnthropicSoftware EngineerBehavioral & LeadershipHR Screen
• Explain batch inference designMedium

  This question tests your ability to design a robust, production-grade batch inference pipeline, focusing on data consistency, system reliability, and scalability.

  AnthropicMachine Learning EngineerMachine LearningOnsite
• Explain career goals and cultural alignmentMedium

  Explain career goals and cultural alignment 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.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Explain management style, execution strategy, and culture choicesHard

  This question evaluates leadership, people-management, execution strategy, and product-oriented decision-making within a machine learning engineering context, covering conflict resolution, low-performer handling, fair calibration, strategy for ambiguous ML products, retrospectives, and culture-fit reasoning.

  AnthropicMachine Learning EngineerBehavioral & LeadershipOnsite
• Explain multithreading vs multiprocessingMedium

  This question tests your conceptual understanding of how process-based and thread-based concurrency models leverage hardware resources and their respective trade-offs in the context of Python and high-performance ML workloads.

  AnthropicMachine Learning EngineerSoftware Engineering FundamentalsOnsite
• Explain projects and handle AI-safety conflictsHard

  This question evaluates leadership, technical ownership, communication, trade-off analysis, ethical reasoning, and judgment on AI safety by asking candidates to describe projects and defend value-driven decisions.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Explain the Most Complex Situation You Have HandledHard

  Explain a complex professional situation through conflicting constraints, personal responsibility, evidence-based decisions, coordination, and lessons from the outcome.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• Extend an LLM Inference Server with a Prefix KV CacheHard

  Anthropic Fellows OA practice: add exact-match, prefix and shared-prefix KV caching with an LRU token budget to an existing LLM inference server, keeping greedy outputs identical.

  AnthropicMachine Learning EngineerML System DesignOnline Assessment
• Fill In the Sampling and Decode Loop of an LLM Inference NotebookMedium

  Anthropic Fellows final-round practice: complete temperature/top-k/top-p sampling and a batched KV-cache decode loop in a Colab notebook so greedy output matches model.generate.

  AnthropicMachine Learning EngineerMachine LearningOnsite
• Find Duplicate FilesMedium

  This question evaluates a candidate's skills in file-system traversal, content deduplication strategies, hashing, large-file I/O handling, concurrency, and robustness to errors and file changes during scans.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Find Duplicate Files by ContentHard

  This question evaluates the ability to parse file-system-style input, group files by identical content, and manage deduplication using appropriate data structures and algorithms.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Find Duplicate Files by Content in a Directory Tree and Explain CPU vs I/O CostsMedium

  Walk a directory tree and report every group of files with identical contents, printing nothing when there are no duplicates. It tests staged filtering by size and hashes, streaming reads of large files, link and error handling, and reasoning about whether the job is CPU-bound or I/O-bound.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsOnsite
• Find a Distributed Mode EfficientlyHard

  Design an exact distributed mode-finding algorithm across partitioned workers that minimizes network I/O, prevents hotspotting, handles deterministic tie-breaking, and contrasts with approximate streaming sketches.

  AnthropicSoftware EngineerSystem DesignOnsite
• Find and remove duplicate filesMedium

  This question evaluates the ability to design scalable file deduplication algorithms, specifically testing knowledge of hashing strategies, collision handling, memory and I/O optimization, handling very large files, incremental/resumable operation, and complexity and trade-off analysis.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Find duplicate files and apply image operationsHard

  This question evaluates skills in string parsing and content-based grouping for duplicate file detection, along with matrix manipulation for image operations like horizontal flips and box blur, covering competencies in algorithm design, data-structure selection, and numerical array transformations.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Find the Slowest Function from Profiler EventsMedium

  This question evaluates the ability to interpret time-ordered profiler event logs, compute inclusive and exclusive function runtimes, reason about call-stack semantics, and recognize anomalies such as recursion, cycles, missing end events, or non-returning functions.

  AnthropicSoftware EngineerCoding & AlgorithmsOnsite
• Fix a Buggy LRU Cache, Then Make It Durable with a Write-Ahead LogMedium

  Debug a flawed least-recently-used cache by finding each bug with a reproducing call sequence and fixing it, then make the cache durable across crashes with a write-ahead log and snapshots. It tests careful code reading, constant-time LRU design, crash-consistent persistence and recovery.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsOnsite
• Fix a Corrupted Bootloader InstructionMedium

  Simulate a virtual machine's instruction set, detect an infinite loop cycle, and find the single instruction modification (between jmp and nop) that allows the program to terminate normally.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Function-Call LRU Cache: Argument Key Generation and Crash Recovery from a LogHard

  A Python exercise that builds an LRU cache for memoizing function calls. Part one asks how to generate a reliable cache key from a function and its arguments; part two adds crash recovery through an append-only log file, testing record framing, durability, torn writes and replay of the cache state.

  AnthropicSoftware EngineerSoftware Engineering FundamentalsOnsite
• Generate Function Profiling Events from Stack SamplesMedium

  Generate function start and end events from timestamped stack samples, including independently debounced frames across consecutive samples. Address transient appearances and absences, changing stack suffixes, shared timestamps, deep stacks, end-of-input behavior, complexity, and online processing.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Generate outputs for images and pipelinesMedium

  This question evaluates algorithmic and systems engineering skills related to batch image processing, parallelization, I/O versus CPU trade-offs, and deterministic output association within the Coding & Algorithms domain.

  AnthropicSoftware EngineerCoding & AlgorithmsTechnical Screen
• Give a Credible Why-This-Organization AnswerMedium

  Build a credible answer to a recruiter asking why a mission-driven AI organization and role matter to you. The framework connects public mission, truthful technical evidence, career timing, transferable skills, knowledge gaps, and a focused question without relying on prestige or invented experience.

  AnthropicSoftware EngineerBehavioral & LeadershipHR Screen
• Guide and override compiler optimizationsHard

  This question evaluates a candidate's competency in low-level performance engineering and compiler optimization techniques—covering pragmas, intrinsics, manual code transformations, profiling and correctness verification—within the System Design and compiler-aware optimization domain.

  AnthropicSoftware EngineerSystem DesignOnsite
• Hiring-Manager Behavioral Round: Impact, Conflict, Cross-Functional Work, and Influencing Without AuthorityMedium

  Tests a candidate's ability to drive technical initiatives across teams, navigate cross-functional disagreements, and exert leadership without formal management authority.

  AnthropicSoftware EngineerBehavioral & LeadershipOnsite
• How do you design an A/B experiment?Hard

  This problem tests your ability to design a rigorous A/B experiment for a consumer-facing AI product, focusing on identifying interference, selecting appropriate metrics, and addressing statistical validity.

  AnthropicSoftware EngineerAnalytics & ExperimentationOnsite