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Frontier Rehearsal · Independent practice · Not affiliated with OpenAI, Anthropic, or xAI.

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Anthropic interview questions for engineering roles

Browse independent practice aligned to Anthropic: coding, system design, and project practice. Use original practice for research-engineering system design, inference APIs, ML systems, batching, reliability, latency, capacity, and observability.

Company

All companies!Show practice for every company.OpenAI!Show OpenAI-aligned question-bank items.Anthropic!Show Anthropic-aligned question-bank items.xAI!Show xAI-aligned question-bank items.Databricks!Show Databricks-aligned question-bank items.Meta!Show Meta-aligned question-bank items.ByteDance!Show ByteDance-aligned question-bank items.Google!Show Google-aligned question-bank items.Scale AI!Show Scale AI-aligned question-bank items.NVIDIA!Show NVIDIA-aligned question-bank items.Cursor!Show Cursor-aligned question-bank items.

Coding System design Behavioral Hiring manager Project deep dive

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CANDIDATE-REPORTED · INDEPENDENT SYNTHESIS

What candidates report about the interview

6 reviewed signals

Use these reports to choose what to rehearse, not to predict a fixed loop. Formats vary by role, team, and date; confirm your current process with the recruiter. Frontier Rehearsal is independent and not company-endorsed.

Process signalsReported stages, formats, timing, and role-specific variations.5

Candidates report that Anthropic's technical screen is usually followed by a decision within about a week, although some candidates wait longer.
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Candidates report that a senior Anthropic loop consisted of two 55-minute rounds, one live coding and one system design; recruiters sometimes skip the CodeSignal assessment for senior or referred candidates, and the coding round leans toward practical system building in Python.
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Candidate reports describe Anthropic's introductory recruiter call as a 20-30 minute conversation covering background, motivation for joining and role expectations, ending with time for candidate questions.
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Candidates report that Anthropic may reuse feedback from a previous onsite when someone reapplies, letting the candidate skip rounds already passed, though the hiring manager still decides whether to proceed.
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According to a candidate report, Anthropic's system-design onsite can include reviewing a design document as part of the interview.
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Candidate briefingsCompact, independently written summaries of fuller candidate timelines.1

According to a candidate report for a senior role, Anthropic's loop ran six interviews across three rounds, weighted system design and personality heavily, and treated the recruiter as someone with real veto power; the same candidate said the process leaned on practical, project-based scenarios rather than classic algorithm puzzles.
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Evidence reviewed Nov 12, 2026. Reports can become stale.

FREE PRACTICE

Public demos, always pinned.

Start with either demo. They remain here when you filter or sort.

Free account: sign in to practice the marked Free questions.

Newest published questions; deterministic, never randomized.

Demo 1Number search with delayed feedback
Demo 2Design real-time device monitoring and control

Question bank filters

Question groups

Filter question bank by interview groupAll 83 Coding 41 System design 10 Behavioral 15 Hiring manager 10 Project deep dive 7

Topics

Filter question bank by topic tagsAll 41 Python 41 Algorithms 6 Data structures 12 Concurrency 4 Distributed systems 2 Storage & state 9 Networking 2 Reliability 4 ML / AI 7 Data / SQL 6 Product design 3 Communication 3

Independent and not affiliated with the companies represented. Collected material does not predict future interview questions.

41 questions

Sort question bankSortFree first Newest Oldest AI ratio ↓AI ratio ↑

Coding results

Every question has a public brief with the task, constraints, and follow-ups. Labels describe interactive practice access.

Topic tags
Python
Data structures
Data / SQL

AnthropicCoding

Python codingWebAssembly + public tests

Reported interview periodOct 2025–May 2026

Design a Weighted, Checkpointable Data Batcher

You are training on several named datasets. A registry can open an iterator for a dataset at a supplied item offset. Design a batcher that mixes examples according to positive integer weights and can resume exa…

Practice focusThe supplied registry exposes get iterator(name, offset=0) , returning an iterator beginning at that dataset's zero-based item offset. It does not advance any other dataset. Implement the batcher, not the registry. Requirements - Every full batch contains exactly batch size examples while data remains available. - Allocate the integral share first, then distribute leftover slots using a deterministic largest-remainder carry across batches. - Break ties by the insertion order of weights and emit examples grouped in that same dataset order.…

Topic tags
Python
Concurrency
ML / AI

AnthropicCoding

Python codingWebAssembly + public tests

Reported interview periodJan 2026–May 2026

Build a Workforce Time and Pay System

Implement employee time tracking, ranking, deferred promotions, and bonus-pay windows. Time is measured in integer ticks.

Practice focusRequirements - Each independent call sequence begins with a new Workforce. Add employees with sequential IDs e1 , e2 , and so on, even when names repeat. Mutation methods return None ; IDs are determined by insertion order rather than returned by add employee. - Inputs use integer, nonnegative timestamps and rates, with rate interpreted as pay per time tick. k is nonnegative; return at most k workers, including zero-work employees. Employee IDs are compared lexicographically for ties. - Count only completed half-open clock sessions [in, out) .…

Topic tags
Python
Concurrency
Distributed systems
Storage & state
Data / SQL

AnthropicCodingHalf AI-generated

Talk-through codingNo local grader

Reported interview periodOct 2025–May 2026

Design Worker and Coordinator Protocols for Distributed Statistics

You control N workers and one coordinator. Worker i can read only shard i. Endpoints communicate by messages; there is no shared mutable dataset. Sending captures an independent snapshot of the payload; later s…

Practice focusThe task is either mode or median . For mode, return the globally most frequent integer with the smaller value winning ties. For median, return the exact middle value as a float, or the mean of the two middle values for even cardinality. The global input is non-empty; individual shards may be empty. Requirements - Define message shapes and an explicit completion condition. - Do not assume the coordinator can read worker data directly. - Avoid shipping complete raw shards in the scalable solution.…

Topic tags
Python
ML / AI

AnthropicCodingHalf AI-generated

Talk-through codingNo local grader

Reported interview periodOct 2025–Apr 2026

Debug a GRPO Training Step

A small PyTorch implementation of Group Relative Policy Optimization runs without exceptions but learns poorly. Inspect and correct its sampling, advantage normalization, and clipped policy loss while preservin…

Practice focusRequired behavior - Convert logits to probabilities with softmax before sampling tokens. - For each prompt group, compute A i = (r i - mean(r)) / (std(r) + eps) using a positive epsilon so zero-variance groups remain finite. - Compute the per-token importance ratio as exp(current logprob - old logprob) . - Exclude masked tokens from both the loss sum and averaging denominator. - Compute the negative mean of the minimum between the unclipped and clipped ratio times advantage. - Return a finite zero loss if every token is masked.…

Topic tags
Python
Algorithms
ML / AI
Product design

Coding exerciseCodingAI-generated

Practice formatRunnable coding45–70 min

Reported interview periodN/A

Build a token-budget batcher

Implement a small batching primitive, then adapt it to padded cost, bounded output, and production reasoning.

Practice focusPython, Greedy algorithms, Boundary cases, ML batching, Complexity through a runnable coding exercise.

Topic tags
Python
Algorithms
Data structures
Storage & state
Networking

Coding exerciseCodingAI-generated

Practice formatRunnable coding45 min

Reported interview periodN/A

Sliding-window rate limiter

Build a limiter, then add quota reporting, retry timing, and independent keys.

Practice focusPython, Queues, State, Boundary cases, Complexity through a runnable coding exercise.

Topic tags
Python
Algorithms
Data structures
Product design
Communication

Coding exerciseCodingAI-generated

Practice formatRunnable coding45 min

Reported interview periodN/A

Interval calendar

Reason about half-open intervals through conflicts, room counts, merging, and free slots.

Practice focusPython, Sorting, Intervals, Heaps, Edge cases through a runnable coding exercise.

Topic tags
Python

Coding exerciseCodingAI-generated

Practice formatRunnable coding45 min

Reported interview periodN/A

Nested dictionary transforms

Flatten and reconstruct nested data, then support path access and lists.

Practice focusPython, Recursion, Data transformation, JSON-like data through a runnable coding exercise.

Topic tags
Python
Storage & state

Coding exerciseCodingAI-generated

Practice formatRunnable coding45 min

Reported interview periodN/A

Command-log store

Evolve a tiny state machine through undo, compaction, and snapshot replay.

Practice focusPython, State machines, Undo logs, Compaction, Persistence through a runnable coding exercise.

Topic tags
Python
Data structures
Reliability
Data / SQL

Coding exerciseCodingAI-generated

Practice formatRunnable coding50 min

Reported interview periodN/A

Dependency-ready scheduler

Turn a dependency graph into stable ready sets, execution layers, and failure impact.

Practice focusPython, Graphs, Stable ordering, Cycle detection, Failure analysis through a runnable coding exercise.

Topic tags
Python
Data structures
Storage & state

Coding exerciseCodingAI-generated

Practice formatRunnable coding50 min

Reported interview periodN/A

Streaming metric window

Compute ordered rolling metrics, then partition state and expose freshness explicitly.

Practice focusPython, Queues, Streaming state, Boundaries, Observability through a runnable coding exercise.

Topic tags
Python
Data / SQL

Coding exerciseCodingAI-generated

Practice formatRunnable coding50 min

Reported interview periodN/A

Evaluation slice aggregator

Aggregate pass rates, enforce minimum support, and compare evaluation runs without hiding weak slices.

Practice focusPython, Aggregation, Evaluation, Statistical hygiene, Determinism through a runnable coding exercise.

Topic tags
Python
Data structures

Coding exerciseCoding

Practice formatWritten response35 minutes

Reported interview periodN/A

Grade book

An in-memory grade book with a ranking that does not treat a missing score as zero.

Practice focusState, Ranking, Edge cases through a written response exercise.

Topic tags
Python
Algorithms

Coding exerciseCoding

Practice formatWritten response35 minutes

Reported interview periodN/A

Vial log

Return the vials that stayed inside a temperature range for their whole stay.

Practice focusScanning, Missing data through a written response exercise.

Topic tags
Python
Algorithms

Coding exerciseCoding

Practice formatWritten response35 minutes

Reported interview periodN/A

Comment batches

Collapse consecutive comments on the same path into one batch.

Practice focusOrder, Grouping through a written response exercise.

Topic tags
Python
Algorithms
Data structures

Coding exerciseCoding

Practice formatWritten response40 minutes

Reported interview periodN/A

Launch order

Return one valid order of steps, or report a cycle.

Practice focusOrdering, Cycles through a written response exercise.

Topic tags
Python

Coding exerciseCoding

Practice formatWritten responseA weekend

Reported interview periodN/A

Notes table

Turn a folder of notes into a table of titles and word counts.

Practice focusA small program, What you left out through a written response exercise.

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CONTENT BOUNDARY

Independent practice aligned to public Anthropic role signals. Includes adaptations of collected interview material and original exercises; not official company questions and not affiliated with the companies represented. Collected material does not predict future interview questions. Report a correction.