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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
- Companies
- 11
- Topics
- 8
- Languages
Showing 1081–1110 of 9403 results
- AnthropicHardSoftware EngineerEstimate FFN Compute, Memory, and Sharding Communication
Estimate standard FFN FLOPs, parameter and activation bytes, and sharding communication, then compare compute, HBM, and network limits with explicit layout assumptions.
ML System DesignOnsite - AnthropicHardSoftware EngineerEstimate VRAM and compare model parallelism
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.
ML System DesignOnsiteDistributed SystemsGPU ArchitectureTransformer Training - AnthropicHardSoftware EngineerExplain AI Safety and Weigh Advanced AI Benefits and Risks
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.
Behavioral & LeadershipHR ScreenAI SafetyAnthropicBehavioral - AnthropicMediumSoftware EngineerExplain Anthropic motivation and leadership stories
This question evaluates leadership, ethical reasoning, cultural fit, communication, and project ownership, targeting Behavioral & Leadership competencies for a software engineer role.
Behavioral & LeadershipOnsite - AnthropicHardSoftware EngineerExplain CPU-Bound vs I/O-Bound Work
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.
Software Engineering FundamentalsTechnical Screen - AnthropicMediumSoftware EngineerExplain How You Know a Professional Referrer
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.
Behavioral & LeadershipHR Screenbehavioralhr-screencommunication - AnthropicHardSoftware EngineerExplain How You Resolved a Professional Conflict
Explain a professional conflict through fair opposing views, shared criteria, a concrete resolution process, personal adjustments, and follow-through.
Behavioral & LeadershipOnsite - AnthropicMediumSoftware EngineerExplain Mission Alignment and Readiness for an Individual-Contributor Role
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.
Behavioral & LeadershipHR ScreenBehavioralMission AlignmentCareer Transition - AnthropicMediumSoftware EngineerExplain Ownership of a Project Business Metric
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.
Behavioral & LeadershipOnsiteBehavioralMetrics & TelemetryEngineering Leadership - AnthropicHardPerformance / Infrastructure Software EngineerExplain Why This Mission and Why Change Roles Now
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.
Behavioral & LeadershipTechnical ScreenBehavioralInfrastructureAI Safety - AnthropicHardSoftware EngineerExplain Your AI Safety Values
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.
Behavioral & LeadershipOnsiteAI SafetyBehavioralRisk Management - AnthropicHardSoftware EngineerExplain Your Motivation and Practical Approach to AI Safety
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.
Behavioral & LeadershipHR ScreenAI SafetyBehavioralAnthropic - AnthropicMediumMachine Learning EngineerExplain batch inference design
This question tests your ability to design a robust, production-grade batch inference pipeline, focusing on data consistency, system reliability, and scalability.
Machine LearningOnsiteSystem DesignMLOpsBatch Processing - AnthropicMediumSoftware EngineerExplain career goals and cultural alignment
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.
Behavioral & LeadershipOnsite - AnthropicHardMachine Learning EngineerExplain management style, execution strategy, and culture choices
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.
Behavioral & LeadershipOnsite - AnthropicMediumMachine Learning EngineerExplain multithreading vs multiprocessing
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.
Software Engineering FundamentalsOnsiteConcurrencyPythonOperating Systems - AnthropicHardSoftware EngineerExplain projects and handle AI-safety conflicts
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.
Behavioral & LeadershipOnsite - AnthropicHardSoftware EngineerExplain the Most Complex Situation You Have Handled
Explain a complex professional situation through conflicting constraints, personal responsibility, evidence-based decisions, coordination, and lessons from the outcome.
Behavioral & LeadershipOnsite - AnthropicHardMachine Learning EngineerExtend an LLM Inference Server with a Prefix KV Cache
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.
ML System DesignOnline AssessmentAnthropic FellowsAI Safety Fellowshipllm-inference - AnthropicMediumMachine Learning EngineerFill In the Sampling and Decode Loop of an LLM Inference Notebook
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.
Machine LearningOnsiteAnthropic FellowsAI Safety Fellowshipllm-inference - AnthropicMediumSoftware EngineerFind Duplicate Files
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.
Coding & AlgorithmsOnsite - AnthropicHardSoftware EngineerFind Duplicate Files by Content
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.
Coding & AlgorithmsOnsite - AnthropicMediumSoftware EngineerFind Duplicate Files by Content in a Directory Tree and Explain CPU vs I/O Costs
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.
Software Engineering FundamentalsOnsite - AnthropicHardSoftware EngineerFind a Distributed Mode Efficiently
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.
System DesignOnsitedistributed-systemssystem-designalgorithms - AnthropicMediumSoftware EngineerFind and remove duplicate files
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.
Coding & AlgorithmsOnsite - AnthropicHardSoftware EngineerFind duplicate files and apply image operations
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.
Coding & AlgorithmsTechnical Screen - AnthropicMediumSoftware EngineerFind the Slowest Function from Profiler Events
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.
Coding & AlgorithmsOnsite - AnthropicMediumSoftware EngineerFix a Buggy LRU Cache, Then Make It Durable with a Write-Ahead Log
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.
Software Engineering FundamentalsOnsite - AnthropicMediumSoftware EngineerFix a Corrupted Bootloader Instruction
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.
Coding & AlgorithmsTechnical Screensimulationgraphdepth-first-search - AnthropicHardSoftware EngineerFunction-Call LRU Cache: Argument Key Generation and Crash Recovery from a Log
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.
Software Engineering FundamentalsOnsite
Anthropic