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Cursor·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
Jun 2026

Summary

Interviewed for a SWE role at Cursor and got asked to build a hash tree algorithm. Pretty sparse on details but it was clearly a technical coding round.

Questions Asked (1)

Q1

Implement a hash tree (Merkle tree) algorithm from scratch.

Algorithms & Data StructuresSystem Design
Author's notes

Not something I'd drilled recently.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: hash function, tree structure (binary or n-ary), and operations (build, update, verify). Then outline the recursive construction: hash leaves, pair and hash upwards until a single root hash. Finally, discuss implementation details, complexity, and potential optimizations.

Pro tip: Mention that Merkle trees enable efficient verification of large data sets and are used in systems like Git and blockchains; this shows you understand real-world applications beyond the algorithm itself.

1. Clarify requirements and constraints

Ask about the hash function (e.g., SHA-256), tree arity (binary is common), and whether the tree needs to support dynamic updates or just static construction. Also confirm if odd number of leaves should be handled by duplicating the last leaf or promoting it.

2. Design the data structure

Define a Node class with hash value, left and right child pointers (for binary tree), and optionally a reference to the original data. For leaves, store the hash of the data block.

3. Implement the build algorithm

Recursively compute hashes: for each leaf, hash the data; for internal nodes, concatenate children's hashes and hash the result. Handle odd number of nodes by duplicating the last node or carrying it up.

4. Implement verification and update operations

For verification, provide a function that takes a leaf, its index, and a proof (list of sibling hashes) to recompute the root. For updates, recompute hashes along the path from the leaf to the root.

5. Analyze complexity and discuss optimizations

State time complexity: O(n) to build, O(log n) to verify or update. Mention space complexity O(n). Discuss optimizations like caching intermediate hashes or using a balanced tree for dynamic updates.

Key Points to Mention

  • Hash function properties: collision resistance, preimage resistance, and determinism.
  • Tree construction: bottom-up hashing, handling odd number of leaves (duplicate last or promote).
  • Proof of inclusion: how to verify a leaf's membership using a Merkle proof (sibling hashes).
  • Complexity: O(n) build, O(log n) verification/update, O(n) space.
  • Use cases: Git, blockchains, distributed systems, and data integrity verification.
  • Edge cases: empty tree, single leaf, duplicate leaves, and tampering detection.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.