Jane Street·Machine Learning Engineer·Onsite - Coding / Algorithms·Senior

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Jun 2026

Summary

Coding round at Jane Street for an ML Engineer role. One problem, but it had enough edge cases to keep me busy for the whole session. The board simulation stuff felt almost like a puzzle game at first, then got annoying fast.

Questions Asked (1)

Simulate a 2D game board where pieces are always inserted at the bottom of a column, pushing existing pieces up. After each insertion attempt, report whether it succeeded, whether any fully-filled column is uniform in piece type, and whether any fully-filled row is uniform in piece type.

Algorithms & Data StructuresSystem Design

Author's notes

My first instinct was to just brute-force scan the whole board after every operation and I said so out loud.

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AI HintsAI Generated

Suggested Approach

Model the board as a grid with column heights and piece types, updating incrementally on each insertion. After each insertion, check only the affected column and row for uniformity, and report success based on column capacity. Use efficient data structures to track column heights and piece types to avoid full scans.

Pro tip: Clarify assumptions upfront: board dimensions, piece types, and whether 'fully-filled' means all cells in that column/row are occupied. Discuss trade-offs between time and space, and mention how you'd handle edge cases like full columns or empty boards.

1. Clarify requirements and constraints

Ask about board size, piece types, insertion rules, and what 'fully-filled' means. Confirm whether rows/columns are checked after every insertion and if multiple uniform lines can exist.

2. Design data structures

Use a 2D array for the board, an array for column heights, and possibly sets to track uniform columns/rows. Consider if piece types are limited (e.g., two colors) to optimize checks.

3. Implement insertion logic

For a given column, check if height < max rows. If so, place piece at next available row, increment height, and update any relevant tracking structures.

4. Check uniformity efficiently

After insertion, check the affected column: if it just became full, verify all pieces in that column are the same type. Similarly, check the row where the piece was placed if that row is now full.

5. Report results and handle edge cases

Return success/failure, and flags for uniform column/row. Discuss handling full columns, multiple uniform lines, and performance for large boards.

Key Points to Mention

Time complexity: O(1) per insertion for updates, O(N) for uniformity check if naive, but can be optimized with counters or hashing.
Space complexity: O(N*M) for board, O(N) for column heights, and additional O(N+M) for tracking uniform lines.
Edge cases: inserting into a full column, board initially empty, multiple uniform columns/rows after one insertion.
Optimization: maintain counts of piece types per column and row to check uniformity in O(1) by comparing counts to dimension.
System design considerations: scalability for large boards, concurrency if multiple insertions, and persistence if needed.
Testing: unit tests for insertion, uniformity checks, and boundary conditions.

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