DoorDash·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

DoorDash ML engineer screen with a grid/heatmap coding problem. Pretty algorithmic for an MLE role but the follow-up about optimizing with a 2D difference array is what made it interesting.

Questions Asked (1)

Given an n x n grid and a list of restaurants, each defined by a position, a radius, and a delivery count, compute a heatmap showing the expected number of deliveries at each cell. Each restaurant contributes its delivery count to every cell within a (2r+1) x (2r+1) square centered on it, clipped to the grid boundaries.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The naive approach is obvious, just loop over each restaurant and fill in the square.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and expected output format, then propose a straightforward solution using a 2D difference array to efficiently add each restaurant's contribution to its square region. After establishing correctness, discuss time and space complexity and potential optimizations for large grids or many restaurants.

Pro tip: Mention that the 2D difference array technique reduces the time complexity from O(n^2 * m) to O(n^2 + m), which is crucial for handling large-scale delivery data at DoorDash. Also, highlight that this approach is easily parallelizable and can be extended to handle weighted contributions or non-uniform delivery distributions.

1. Clarify Requirements and Constraints

Ask about grid size limits, number of restaurants, whether delivery counts are integers, and if the heatmap should be normalized or raw counts. Confirm the exact definition of the square region (centered, clipped).

2. Outline a Naive Approach

Describe a brute-force method: for each restaurant, iterate over all cells in its square and add the delivery count. Analyze its time complexity O(m * r^2) and note it may be inefficient for large grids or many restaurants.

3. Propose an Optimized Solution Using 2D Difference Array

Explain how to use a 2D difference array to apply each restaurant's contribution in O(1) time per restaurant, then compute the prefix sum to get the final heatmap in O(n^2) time. Detail the update steps for the four corners of the square.

4. Analyze Complexity and Trade-offs

Compare the naive and optimized approaches in terms of time and space. Discuss when the naive approach might be acceptable (e.g., small grids or few restaurants) and when the optimized approach is necessary.

5. Discuss Extensions and Practical Considerations

Mention potential extensions like handling non-square regions, weighted contributions, or dynamic updates. Also, consider memory usage and whether the heatmap can be computed in a streaming fashion.

Key Points to Mention

2D difference array technique for efficient range updates
Time complexity: O(m + n^2) vs. naive O(m * r^2)
Space complexity: O(n^2) for the heatmap and difference array
Clipping the square to grid boundaries
Potential for parallelization or distributed computing for large-scale data
Edge cases: restaurants with radius 0, overlapping squares, and grid boundaries

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