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Jane Street·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Coding round for an MLE role at Jane Street. One question, but it was a lot more implementation-heavy than I expected from a finance firm.

Questions Asked (1)

Q1

Build a simplified code editor (think VSCode-style) that supports collapsing and expanding code blocks. For example, given a 5-line snippet where lines 2 through 4 form a for-loop block, calling shrink(2) should collapse that block down to just its header line, resulting in a 3-line view.

Algorithms & Data StructuresSystem Design
Author's notes

I spent the first few minutes just trying to figure out the right data structure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and edge cases, then propose a data structure that efficiently supports collapsing and expanding blocks. Implement the core operations (shrink and expand) with careful handling of nested blocks and line mapping, and analyze time/space complexity.

Pro tip: Demonstrate awareness of real-world editor behavior: collapsing a block should hide its contents but preserve the ability to expand it later, and nested blocks require a stack or tree structure to manage visibility correctly.

1. Clarify Requirements and Edge Cases

Ask questions to understand the expected behavior: What defines a block? How are blocks identified (e.g., by indentation, braces)? What happens when collapsing a block that contains other blocks? Can multiple blocks be collapsed independently?

2. Choose Data Structures

Select a representation for the code and blocks. A tree of blocks (each with start/end lines) or an interval tree can efficiently manage nested blocks. For line mapping, consider a balanced BST or a Fenwick tree to track visible lines.

3. Design Core Operations

Define shrink(line) to find the innermost block starting at that line, mark it collapsed, and update the visible line count. Define expand(line) to reverse this. Ensure operations handle nested blocks by updating parent blocks' visible line counts.

4. Implement and Test

Write pseudocode or actual code for the operations, using the chosen data structures. Test with simple cases (single block), nested blocks, and multiple independent blocks. Verify that line numbers adjust correctly after collapse/expand.

5. Analyze Complexity and Optimize

Discuss time and space complexity of shrink and expand. If using a tree, operations might be O(log n) or O(depth). Consider optimizations like lazy updates or caching visible line counts for frequently accessed blocks.

Key Points to Mention

  • Use a tree or interval-based data structure to represent nested code blocks.
  • Maintain a mapping between original line numbers and visible line numbers, updating it on collapse/expand.
  • Handle nested blocks by propagating visibility changes to ancestor blocks.
  • Consider edge cases: collapsing an already collapsed block, expanding a non-collapsed block, blocks at file boundaries.
  • Analyze time complexity: ideally O(log n) per operation with balanced trees, or O(depth) with simpler structures.
  • Discuss trade-offs between different data structures (e.g., simple list vs. tree) for scalability.

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