Meta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jul 2026

Summary

Meta SWE coding round with an ads ranking problem. Pretty domain-specific for a coding question, felt more like a product-adjacent algo problem than pure leetcode.

Questions Asked (1)

Given a list of ads with CTR, Bid, and Expected Clicks metrics, implement a function that returns the ad IDs sorted by rank. Sort primarily by CTR descending, then Bid descending as a tiebreaker, then Expected Clicks descending if bids also tie.

Algorithms & Data StructuresProduct Analytics & Metrics

Author's notes

The multi-key sort itself isn't hard, Python's tuple sort makes it pretty clean.

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AI HintsAI Generated

Suggested Approach

Clarify the input format and sorting requirements, then propose a custom comparator that sorts by CTR descending, then Bid descending, then Expected Clicks descending. Implement the solution efficiently, considering time and space complexity, and test with edge cases like ties and empty input.

Pro tip: Mention that you would use a stable sort or a custom comparator to ensure correct tie-breaking, and discuss how to handle floating-point precision issues when comparing CTR values.

1. Clarify requirements and input/output

Ask about the data structure for ads (e.g., list of objects), the types of metrics (float vs. int), and the expected output format (list of ad IDs). Confirm the sorting order and tie-breaking rules.

2. Design the comparator

Define a comparison function that first compares CTR in descending order, then Bid in descending order, then Expected Clicks in descending order. Consider using a tuple or a custom comparator.

3. Implement the sorting algorithm

Use a built-in sort with the custom comparator (e.g., Python's sorted with key or cmp_to_key). Discuss time complexity (O(n log n)) and space complexity (O(n) for sorting).

4. Handle edge cases and precision

Address potential floating-point precision issues by using a tolerance or converting to integers if possible. Test with empty lists, single element, and multiple ties.

5. Test and validate

Walk through a small example to verify the sorting order. Consider writing unit tests for various scenarios.

Key Points to Mention

Time and space complexity of sorting (O(n log n) time, O(n) space).
Use of a custom comparator or key function to handle multiple sorting criteria.
Handling floating-point precision when comparing CTR values.
Stability of sorting algorithms and its relevance to tie-breaking.
Edge cases: empty input, single ad, all metrics equal, negative values (if applicable).
Potential optimization: if data is large, consider external sorting or distributed sorting.

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