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Harvey AI

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Harvey AI is a legal technology startup that builds generative AI tools tailored for law firms and legal professionals. It is known for its domain-specific AI assistant designed to help with legal research, drafting, and analysis.

7 interview notes · updated Jul 2026

Harvey AI·Software Engineer·Technical Phone Screen

Jul 2026
Harvey AI technical screen for a software engineering role, basically one big design question that spiraled into a lot of edge case discussion. Felt like a coding round dressed up as a system design question.
  • Design an in-memory hierarchical file system that supports adding files via absolute paths (creating intermediate directories automatically) and listing a directory's immediate children in sorted order. The system must enforce a max of 5 entries per directory, reject insertions that would exceed it, and auto-rename duplicate filenames by appending a counter before the extension. Describe your data structures, parsing logic, and time/space complexity, and walk through edge cases like root paths, trailing slashes, and invalid inputs.

“This looked like a trie problem at first and I went straight to implementing nodes with a children map.”

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Harvey AI·Software Engineer·Onsite - System Design / Architecture

Jun 2026
System design round at Harvey AI for a software engineer role. The whole thing was one big question about building a file storage service from scratch, and they wanted you to go deep on basically everything: APIs, metadata, concurrency, scaling, security. Felt like a 45-minute gauntlet.
  • Design a production-grade file storage service with addFile(path) and list(path) APIs, where each directory is capped at 5 entries and duplicate filenames are auto-renamed with OS-style suffixes. Walk through the full architecture including API layer, metadata service, and content store.
  • What metadata schema and storage backend would you choose for this service, and why relational vs NoSQL?
  • How do you handle large file uploads, and what changes when files are too big to buffer in memory?
  • How would you handle consistency, failure scenarios, and rollback in this system?
  • How would you scale this service? Talk through partitioning, sharding, and caching strategies.
  • What observability, rate limiting, and quota enforcement would you build into this service?
  • How would you approach security for this service, including authentication, authorization, path traversal protection, and encryption?
  • Define the key SLIs and SLOs you'd set for this service.

“This is a beast of a question.”

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Harvey AI·Software Engineer·Technical Phone Screen

May 2026
Got a coding question at Harvey AI for a software engineer role that was basically about string matching and annotation. Three parts, each building on the last, and the third part is where things got interesting.
  • Given an LLM output string and a list of source strings, design an algorithm to count how many times each source string appears in the LLM output. Then extend it to wrap every occurrence of a source in XML-style tags, handling overlapping matches correctly. Finally, modify the tagging so each tag includes annotations indicating which source indices matched at that position.

“The first part is basically substring frequency counting, fine.”

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Harvey AI·Software Engineer·Onsite - System Design / Architecture

May 2026
System design round at Harvey AI for a software engineering role. The whole interview was basically one long question about file deduplication that kept branching into harder and harder territory. Walked out not totally sure how I did.
  • How would you determine whether two files have identical byte content, ignoring filesystem metadata? Design a hashing-based approach and extend it to large-scale deduplication across machines.

“This question had like seven sub-parts and I did not fully appreciate that until I was already three minutes into talking about SHA-256 vs MD5.”

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Harvey AI·Software Engineer·Technical Phone Screen

Apr 2026
Harvey AI coding round, one big design question that took the whole session. The problem looked like a toy filesystem at first but the constraints turned it into a mess of edge cases I was not fully prepared for.
  • Design and implement an in-memory hierarchical file system with addFile and get operations, a per-directory capacity limit of 5 entries, and OS-style auto-renaming for duplicate file names within the same directory.

“The rename logic is where I lost time.”

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Harvey AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Technical round at Harvey AI for an ML engineer role, centered on a pretty involved NLP-adjacent problem. No ML libraries allowed which made it more interesting than I expected.
  • Given an input string, design a tree or graph-based algorithm to transform it into a target tokenized sequence. You must simulate embeddings without using any ML models, and validate the final output.

“This one took me a minute to even parse what they were asking.”

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Harvey AI·Machine Learning Engineer·Technical Phone Screen

Apr 2026
ML engineer interview at Harvey AI that centered on a technical deep-dive into a past AI project. Pretty much the whole conversation was about one thing: walk us through something you built, then defend every choice you made.
  • Walk us through an AI-focused project you've worked on previously.
  • What alternative approaches did you consider, and why did you go with the technologies you chose?
  • What were the pros and cons of the approach you took, and how well did it actually solve the problem?

“I had something prepped but the follow-up questions are where it gets uncomfortable.”

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