Meta·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jul 2026

Summary

Meta ML engineer coding round, basically one meaty array problem with a follow-up that I did not see coming. The core question was fine but the streaming extension is what really separated people I think.

Questions Asked (2)

Given an unsorted array of up to a million integers and an integer k, return the k smallest elements in ascending order. Your solution should run in O(n log k) time and use O(k) extra space using a heap. How do you handle duplicates and negative numbers?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I jumped straight to a max-heap of size k and walked through it, which was the right call.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then explain the heap-based solution: maintain a max-heap of size k while iterating through the array, and finally extract elements to return in ascending order. Discuss how duplicates and negative numbers are naturally handled by the heap's comparison logic, and analyze time and space complexity.

Pro tip: Mention that using a max-heap of size k is optimal for O(n log k) time and O(k) space, and that duplicates are preserved because the heap stores values, not unique keys. Also note that negative numbers require no special treatment as long as the comparison operator is correct.

1. Clarify requirements and edge cases

Confirm the input size, data types (including negatives), and whether duplicates should be preserved. Ask about expected output format and if k can be larger than n.

2. Explain the heap-based algorithm

Describe maintaining a max-heap of size k: for each element, if heap size < k, push; else if element < heap top, pop and push. This keeps the k smallest elements seen so far.

3. Handle duplicates and negatives

Explain that duplicates are naturally handled because the heap stores individual values; negative numbers compare correctly with standard integer comparison. No special logic is needed.

4. Analyze complexity and trade-offs

State that time complexity is O(n log k) due to heap operations, and space is O(k). Compare with alternatives like sorting (O(n log n)) or quickselect (O(n) average but O(n) space).

5. Discuss final sorting and implementation details

Mention that after processing, the heap contains the k smallest elements but not in sorted order; extract them and sort ascending (O(k log k)) or use a min-heap. Note that O(k log k) is dominated by O(n log k) when n >> k.

Key Points to Mention

Use a max-heap of size k to efficiently track the k smallest elements.
Duplicates are preserved because the heap stores values, not unique identifiers.
Negative numbers require no special handling; standard comparison works.
Time complexity: O(n log k) for heap operations, plus O(k log k) for final sort (negligible if n >> k).
Space complexity: O(k) for the heap.
Edge cases: k=0, k>=n, empty array, and large n (1 million) with memory constraints.

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

Follow-up: how would you redesign this for a streaming scenario where integers arrive one at a time and you need to support a getSmallestK() call at any point?

Algorithms & Data StructuresSystem Design

Author's notes

Completely blanked for a second.

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AI HintsAI Generated

Suggested Approach

First, clarify the requirements: streaming integers, need to support getSmallestK() at any point, and likely handle large volume. Then, propose a data structure like a max-heap of size K to maintain the K smallest elements, and discuss how to handle updates and queries efficiently. Finally, analyze time and space complexity, and consider trade-offs with alternative approaches like balanced BSTs or sorted containers.

Pro tip: Mention that for streaming, you need to consider the frequency of getSmallestK() calls and whether K is fixed or dynamic; if K is small, a heap is ideal, but if K is large, other structures might be better. Also, discuss how to handle duplicates and potential memory constraints.

1. Clarify Requirements

Ask about the expected frequency of getSmallestK() calls, whether K is fixed, the range of integers, and memory constraints. This shows you think about practical constraints.

2. Propose Data Structure

Suggest using a max-heap of size K to store the K smallest elements seen so far. For each incoming integer, compare with the heap's root and adjust accordingly.

3. Detail Operations

Explain insertion: if heap size < K, add; else if new integer < heap root, replace root and heapify. For getSmallestK(), return the heap contents (or sorted if needed).

4. Analyze Complexity

Insertion takes O(log K) time, getSmallestK() takes O(K) to return or O(1) if just the root is needed. Space is O(K).

5. Discuss Trade-offs and Alternatives

Mention alternatives like balanced BST (O(log N) per insert, O(K) to retrieve) or maintaining a sorted list (O(N) insert). Highlight that heap is optimal for small K and frequent queries.

Key Points to Mention

Use a max-heap of size K to efficiently maintain the K smallest elements.
Insertion: compare with heap root, replace if smaller, then heapify.
getSmallestK(): return heap elements; if sorted order needed, sort them (O(K log K)).
Time complexity: O(log K) per insertion, O(K) for getSmallestK() (or O(1) if only the smallest is needed).
Space complexity: O(K).
Consider duplicates: heap handles them naturally; if unique elements required, use a set alongside.
Trade-offs: heap is best for small K; for large K or frequent sorted retrieval, consider a balanced BST or order-statistic tree.

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