Upstart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Went through a coding round at Upstart for a QA Engineer role and got hit with what felt like a classic SWE problem. Not the most QA-flavored interview I've had, but it was fine.

Questions Asked (1)

Given an array of integers and a target value, return the indices of the two numbers that sum to the target. Walk through both a brute-force approach and an optimized solution, and discuss follow-up variants like handling duplicates, sorted arrays, or counting all valid pairs.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Started with the nested loop approach just to show I understood the baseline, then moved to the hash map version where you check the complement on each pass.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., exactly one solution, can't use same element twice, return any valid pair). Then present a brute-force O(n²) solution using nested loops, followed by an optimized O(n) solution using a hash map. Finally, discuss follow-up variants by explaining how the approach changes for duplicates, sorted arrays, or counting all pairs.

Pro tip: Always clarify assumptions and edge cases before coding; for example, ask if the array is sorted, if there are duplicates, or if multiple pairs exist. This shows you think like a production engineer, not just a competitive programmer.

1. Clarify the problem

Ask questions to understand constraints: input size, sortedness, duplicates, multiple solutions, and whether indices or values are needed. Confirm that each element can be used only once.

2. Brute-force approach

Explain the O(n²) solution: iterate over each element and check every other element for a complement. Mention its simplicity but inefficiency for large inputs.

3. Optimized hash map approach

Describe the O(n) solution: use a hash map to store each element's index as you iterate. For each element, check if target - element exists in the map; if so, return the pair.

4. Discuss trade-offs and edge cases

Compare time/space complexity of both approaches. Highlight that the hash map uses O(n) extra space but is faster. Mention edge cases like empty array, no solution, or negative numbers.

5. Address follow-up variants

Explain how to handle duplicates (store list of indices or count frequencies), sorted arrays (two-pointer technique), and counting all pairs (use frequency map and combinatorics).

Key Points to Mention

Time and space complexity of brute-force (O(n²) time, O(1) space) vs. hash map (O(n) time, O(n) space).
Handling duplicates: either store multiple indices in the hash map or use a frequency counter.
Sorted array variant: use two pointers (left and right) to find pairs in O(n) time and O(1) space.
Counting all valid pairs: use a hash map to count frequencies, then for each unique number, add combinations (e.g., n choose 2) for pairs that sum to target.
Edge cases: empty array, single element, no solution, multiple solutions, negative numbers, and integer overflow.
Clarifying questions: ask about input constraints, expected output format, and whether the array is sorted or contains duplicates.

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