DoorDash·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

DoorDash ML engineer interview with a greedy interval batching problem. The coding part was pretty focused and they wanted you to actually run your own tests, which I wasn't totally expecting.

Questions Asked (1)

Given a sorted list of timepoints, pack them into the minimum number of batches where each batch holds at most B items and all items fall within a window of length W starting from the first item in that batch. Implement the solution, write your own test cases, run them, and then discuss edge cases and possible improvements.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The greedy part clicked pretty fast: start a batch at the leftmost unused item, keep pulling in items that fit within B count and within the W window, then close the batch and repeat.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a greedy algorithm that iterates through the sorted list, forming batches by taking up to B items within a window of length W from the first item in the batch. Implement the solution with clean code, write comprehensive test cases including edge cases, run them, and discuss potential improvements such as handling streaming data or optimizing for specific distributions.

Pro tip: Emphasize that the greedy approach is optimal for minimizing batches because any valid batch must start at the first unpacked item, and taking as many items as possible within the window never hurts future batches. This demonstrates strong algorithmic reasoning.

1. Clarify requirements and constraints

Ask about input format (e.g., list of integers or timestamps), whether B and W are inclusive, and if the list is guaranteed sorted. Confirm expected output (e.g., list of batches or just count).

2. Design the greedy algorithm

Iterate through the sorted list, and for each batch, take the first unpacked item as the window start, then include subsequent items until either B items are taken or the next item exceeds the window length W.

3. Implement and test

Write clean code with clear variable names. Create test cases covering normal scenarios, edge cases (empty list, B=1, W=0, all items within W, etc.), and run them to verify correctness.

4. Analyze complexity and edge cases

Discuss time complexity O(n) and space complexity O(1) extra (excluding output). Mention edge cases like duplicate timestamps, very large B or W, and negative timestamps if applicable.

5. Discuss improvements and trade-offs

Suggest potential optimizations for streaming data, parallelization, or alternative approaches if the list is not sorted. Discuss trade-offs between batch size and window constraints.

Key Points to Mention

Greedy algorithm is optimal because any batch must start at the first unpacked item, and taking as many as possible within the window cannot increase the number of batches needed.
Time complexity is O(n) since each item is processed once; space complexity is O(1) extra if only counting batches, or O(n) if storing batches.
Edge cases: empty list, B=1 (each item in its own batch), W=0 (only items with same timestamp can be batched), items exactly at window boundary (inclusive vs exclusive).
Test cases should include: normal case, all items fit in one batch, each item requires separate batch, and mixed scenarios.
Possible improvements: handle streaming input with a sliding window, use binary search to find the window end for each batch (though O(n) is already optimal), or adapt for unsorted input by sorting first (O(n log n)).
Real-world application: batching time-series data for model training or inference, where batches must respect time windows and size limits.

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