Katkat

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Harvey interview questions

46 practice questions in the style of Harvey's interviews, covering Software Engineering Fundamentals, Coding & Algorithms, Behavioral & Leadership, System Design, ML System Design, Machine Learning.

Questions46

0 easy38 medium8 hard

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Harvey interview questions by the numbers

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Katkat has 46 Harvey interview questions. The largest topics are Coding & Algorithms (35%), System Design (20%) and Software Engineering Fundamentals (17%). By difficulty: 83% medium and 17% hard. The most common round is Onsite (52%). Frequent concepts include System Design, Design, Information Retrieval and LLM.

Difficulty

  • Medium83% · 38
  • Hard17% · 8

Interview rounds

  • Onsite52% · 24
  • Technical Screen46% · 21
  • Other2% · 1

Roles

  • Software Engineer80% · 37
  • Backend Engineer9% · 4
  • Frontend Engineer4% · 2
  • Machine Learning Engineer4% · 2
  • AI Agent2% · 1

Frequent concepts

  • System Design11% · 5
  • Design9% · 4
  • Information Retrieval9% · 4
  • LLM9% · 4
  • RAG9% · 4
  • design7% · 3

Showing 1–30 of 46 results

  • Design and implement a virtualized React spreadsheet component supporting editable cells, formula evaluation with string concatenation, cycle detection, and targeted re-rendering.

    HarveyFrontend EngineerSoftware Engineering FundamentalsTechnical Screen
  • Design an in-memory hierarchical file vault supporting automatic directory creation, lexicographical directory listings, and duplicate-name resolution using the smallest available numeric suffix.

    HarveySoftware EngineerCoding & AlgorithmsOnsite
  • Prepare a project decision deep dive with concrete alternatives, personal ownership, correctness evidence, accepted costs, and a migration rationale.

    HarveySoftware EngineerBehavioral & LeadershipOnsite
  • This question evaluates leadership, ownership, collaboration, technical judgment, and project management skills within the Behavioral & Leadership domain of software engineering.

    HarveySoftware EngineerBehavioral & LeadershipTechnical Screen
  • This question tests your ability to design a scalable cloud file storage and collaboration platform, focusing on the architectural separation between metadata and blob storage, chunking, deduplication, and granular access control.

    HarveySoftware EngineerSystem DesignOnsite
  • Design drive-style file storage with metadata and blob separation, ACL checks, resumable chunk uploads, atomic publication, and version conflicts.

    HarveySoftware EngineerSystem DesignOnsite
  • Design an enterprise-grade retrieval-augmented generation (RAG) service for confidential document vaults. The question tests multi-tenant access control, precise citation provenance, document versioning/freshness, retrieval quality, and hallucination/injection defenses.

    HarveySoftware EngineerML System DesignOnsite
  • This question evaluates ability to design and implement data structures and algorithms for expression parsing, dependency tracking, incremental evaluation, and cycle detection in an in-memory spreadsheet.

    HarveySoftware EngineerCoding & AlgorithmsOnsite
  • Design persistent real-time chat with conversation ordering, idempotent sends, paginated history, optimistic state, delivery receipts, and reconnect catch-up.

    HarveyFrontend EngineerSystem DesignOther
  • This question evaluates competency in designing large-scale distributed file storage and collaboration systems, covering storage and metadata modeling, API design, synchronization and consistency models, resumable large-file handling, versioning, access control, scalability, availability, durability, and security.

    HarveySoftware EngineerSystem DesignTechnical Screen
  • This problem evaluates your ability to design a RAG-based Q&A system for sensitive legal documents, focusing on multi-stage retrieval, hallucination prevention, and data privacy.

    HarveySoftware EngineerML System DesignOnsite
  • Design an end-to-end Retrieval-Augmented Generation (RAG) system for querying confidential, complex legal memos. This evaluates domain-specific parsing, hybrid retrieval with fine-grained access control, and grounded generation with strict citation fidelity.

    HarveySoftware EngineerML System DesignOnsite
  • Design an enterprise-grade Retrieval-Augmented Generation (RAG) system capable of answering complex natural language queries over millions of internal documents with high precision, low latency, and verifiable citations.

    HarveySoftware EngineerSystem DesignOnsite
  • Design an enterprise-grade document evaluation agent that evaluates legal and policy compliance against structured playbooks, producing traceable citations, revision proposals, and tier ratings.

    HarveyAI AgentML System DesignOnsite
  • Design an enterprise-grade virtual data room (VDR) for secure legal document sharing, focusing on multi-tenant access control, instant permission revocation, dynamic watermarking, and tamper-evident audit logging.

    HarveySoftware EngineerSystem DesignOnsite
  • This question evaluates design and implementation skills for hierarchical in-memory data structures, string parsing and file-name collision handling under constraints, and the ability to reason about time and space complexity.

    HarveySoftware EngineerCoding & AlgorithmsTechnical Screen
  • This question evaluates a candidate's competency in distributed systems and storage architecture, covering metadata schema design, concurrency control for atomic per-directory limits and renaming, transaction boundaries and idempotency, storage models for file bytes, large-file handling, consistency and failure modes, scalability and partitioning, observability, lifecycle management, and security. It is in the System Design domain and is commonly asked because it reveals how a candidate reasons about architectural trade-offs and practical implementation concerns; it tests both high-level conceptual design and hands-on practical application.

    HarveySoftware EngineerSystem DesignTechnical Screen
  • This question evaluates a candidate's competencies in secure backend system design, covering authentication and authorization, metadata and object storage architectures, integrity verification for chunked uploads, scalability, and operational reliability.

    HarveyBackend EngineerSystem DesignOnsite
  • Design an in-memory spreadsheet engine that supports cell updates, formula evaluations, cycle detection, and O(1) value lookups via proactive cache propagation.

    HarveySoftware EngineerCoding & AlgorithmsTechnical Screen
  • Design an LLM agent system that revises a given contract so it complies with several dozen given legal rules. It tests decomposing work across model calls, representing edits so they can be traced and merged, resolving overlapping or conflicting changes, per-rule verification, and cost and latency trade-offs.

    HarveySoftware EngineerML System DesignOnsite
  • This question evaluates the ability to design and implement hierarchical in-memory data structures, manage capacity constraints and OS-style duplicate naming, and reason about edge cases and time/space complexity.

    HarveySoftware EngineerCoding & AlgorithmsTechnical Screen
  • Design a RAG pipeline with document chunking, retrieval recall evaluation, grounded answers, permission-aware context, and versioned index updates.

    HarveySoftware EngineerML System DesignOnsite
  • A Harvey AI system design screen on determining whether two files are byte-identical while ignoring metadata, then scaling to deduplication. It covers hashing algorithm choice (SHA-256/BLAKE3 vs MD5/xxHash), multi-stage verification, collision and false-positive mitigation, streaming and parallel hashing, content-defined chunking, and persistent hash-index/service design.

    HarveySoftware EngineerSystem DesignTechnical Screen
  • Implement eager spreadsheet formulas with constant-time cached reads, transitive dependency updates, cycle rejection, and atomic definition replacement.

    HarveySoftware EngineerCoding & AlgorithmsTechnical Screen
  • Implement embedding-based document retrieval with cosine similarity in a Python notebook, then explain how cosine similarity relates to dot product and Euclidean distance and how to measure retrieval quality. It tests vectorized top-k search, deterministic ties, index metric choice and evaluation metrics.

    HarveySoftware EngineerMachine LearningOnsite
  • Implement an expression evaluator for a dictionary of symbolic definitions with arithmetic operations, resolving dependencies, and detecting cycles and undefined references.

    HarveySoftware EngineerCoding & AlgorithmsOnsite
  • This question evaluates proficiency in technical decision-making, trade-off analysis, and system design ownership for backend projects, including architecture rationale, technology selection, and the candidate's specific role and measurable impact.

    HarveyBackend EngineerSoftware Engineering FundamentalsOnsite
  • Design and implement a robust file duplicate finder that handles filesystem cycles, minimizes disk I/O using multi-stage filtering, and avoids loading entire large files into memory.

    HarveySoftware EngineerSoftware Engineering FundamentalsTechnical Screen
  • This question evaluates skills in string matching, pattern recognition, overlap handling, text annotation, and counting occurrences across multiple source strings.

    HarveySoftware EngineerCoding & AlgorithmsTechnical Screen
  • Tests string search algorithms, interval merging techniques, and clean API design for formatting highlighted spans in text without redundant or malformed tags.

    HarveySoftware EngineerSoftware Engineering FundamentalsTechnical Screen

Harvey interview experiences

All 6 →
  • Patterns from Seventeen ReportsHarvey · Software Engineer · Onsite · Sep 2026

    The author summarizes 17 Harvey interview reports from March through September 2026. Technical screens reportedly centered on spreadsheet formulas or text highlighting, with attention to dependency cycles, evaluation timing, and whole-word matching. Onsite coding examples included an in-memory filesystem, expression evaluation, citation highlighting, and duplicate-file discovery with symbolic-lin…

  • Phone Screen Building an Excel set_cell/get_cell in Three ScopesHarvey · Software Engineer · Technical Screen · Sep 2026

    Another Excel set_cell and get_cell problem. It starts out really simple and then ramps up step by step, and time was still really tight while writing it. Scope 1 Implement a simple Excel. You need and In step one, set only takes numbers, no formulas, so I'd suggest getting through it fast and not wasting time. Scope 2 Step two adds the ability to set a formula, e.g. set_cell("A1", "=B1+C1+1") or…

  • Hiring Manager Screen, Then a Spreadsheet Tech Screen I Didn't FinishHarvey · Software Engineer · Technical Screen · Aug 2026
    Rejected

    I applied online and a recruiter reached out. First round was a hiring manager screen. They asked questions like: tell me about a project I owned in the last year tell me about tough feedback I received tell me about a time I discovered an important problem outside of my immediate focus area Second round was the technical screen. It was similar to the spreadsheet question you can find online, but…

  • Three Rounds and a Frustrating Empty OfficeHarvey · AI Agent · Onsite · Aug 2026
    Rejected

    It was a surreal onsite experience. I had practiced the phone-screen problem many times. It was the Google Sheets problem, and I finished it immediately. The recruiter told me about an hour later that I had passed, then arranged the onsite. There were three rounds. For system design, I was asked to design an agent that scores a file according to a set of rules and labels it qualified, acceptable,…

  • A File System Question and a Hard-to-Follow InterviewerHarvey · Software Engineer · Technical Screen · Sep 2025

    The file system question from 1point3acres. The writeup is here: a question that hasn't shown up on the forum before, basically a file system where you implement two functions: addFile(String path) — given a string like "path/to/somewhere/file.txt", store this file getFile(String path) — input "path/to/somewhere" returns file.text, input "path/to" returns the "somewhere" folder There wasn't reall…

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