Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026Remote

Summary

Instacart SWE interview via Karat, building on a sports-stats coding problem from a prior round. The follow-up asked me to extend the solution I'd already written, which sounds easier than it is when you're mid-session and second-guessing your earlier code.

Questions Asked (1)

Given a 2D structure where rows are athletes and columns are events, implement a function that returns the single best score for a specific event across all athletes. For each athlete, consider only their completed attempts and take their highest score, then return the maximum of those across all athletes.

Algorithms & Data Structures

Author's notes

The tricky part wasn't the logic, it was that this was a continuation problem.

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AI HintsAI Generated

Suggested Approach

Clarify the data structure and edge cases, then propose a single-pass solution that iterates through each athlete, filters completed attempts, finds the max score for the event, and tracks the global maximum. Discuss time and space complexity, and consider optimizations like early termination or handling missing data.

Pro tip: Demonstrate awareness of real-world data issues: mention that scores might be null, negative, or non-numeric, and that 'completed' status must be explicitly checked. Also, discuss how the solution scales if the data is huge (e.g., streaming or distributed processing).

1. Clarify requirements and assumptions

Ask about the data format: how are scores stored? What does 'completed attempt' mean? Are there missing values? Confirm the event column index and that we need the maximum score across all athletes.

2. Outline the algorithm

Explain that you will iterate over each athlete, extract their scores for the given event, filter out incomplete attempts, compute the maximum for that athlete, and then take the maximum across all athletes.

3. Handle edge cases

Discuss what happens if no athlete has a completed attempt for the event (return null or throw exception), if scores are negative, or if the data is empty. Also consider athletes with no attempts.

4. Analyze complexity and optimize

State that the time complexity is O(N*M) in the worst case (N athletes, M events) but can be O(N) if we only look at the specific event column. Space complexity is O(1). Mention potential optimizations like early termination if a maximum possible score is known.

5. Write pseudocode or code

Provide clear pseudocode or actual code (e.g., in Python) that implements the solution, using descriptive variable names and comments. If coding, handle nulls and incomplete attempts properly.

Key Points to Mention

Data structure traversal: iterate rows (athletes) and access the specific event column.
Filtering: only consider attempts marked as 'completed' (e.g., status flag or non-null score).
Per-athlete maximum: compute the highest score for the event for each athlete.
Global maximum: track the overall best score across all athletes.
Edge cases: no completed attempts, empty input, negative scores, missing data.
Time and space complexity: O(N) time (if only one event column) and O(1) space.

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