Whatnot·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026Remote

Summary

Whatnot software engineer screen, two-part coding problem on dot products. Part one was straightforward, part two pushed into sparse vector territory which is where things got more interesting. No behavioral stuff, just the problem.

Questions Asked (2)

Implement a function that computes the dot product of two equal-length integer arrays.

Algorithms & Data Structures

Author's notes

Pretty easy warmup.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints (e.g., array sizes, integer ranges, potential overflow) and then propose a simple iterative solution that computes the sum of products. Discuss time and space complexity, and consider edge cases like empty arrays or mismatched lengths.

Pro tip: Mention that you would use a 64-bit integer for the accumulator to prevent overflow, and discuss how to handle mismatched lengths gracefully (e.g., throw an exception or return a sentinel value). This shows attention to robustness and real-world concerns.

1. Clarify requirements

Ask about input constraints: array lengths (equal?), integer ranges (could overflow?), and expected behavior for invalid inputs (e.g., mismatched lengths).

2. Outline approach

Propose a single-pass iterative solution: initialize a sum to 0, loop through indices, multiply corresponding elements, and add to sum.

3. Analyze complexity

State that time complexity is O(n) and space complexity is O(1), which is optimal for this problem.

4. Handle edge cases

Discuss handling empty arrays (return 0), mismatched lengths (throw IllegalArgumentException), and potential integer overflow (use long accumulator).

5. Write code

Implement the function in a clean, readable manner, possibly with comments explaining key decisions.

Key Points to Mention

Time and space complexity analysis
Handling of edge cases (empty arrays, mismatched lengths)
Prevention of integer overflow by using a wider accumulator type
Input validation and error handling strategy
Potential for parallelization or vectorization for large arrays (optional optimization)
Clarity and readability of the implementation

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

Now assume the vectors are very long but mostly zeros. Represent them as sorted lists of (index, value) pairs and compute the dot product efficiently without iterating over the full length.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

This is where I stumbled a bit.

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AI HintsAI Generated

Suggested Approach

Explain that since both vectors are sparse and represented as sorted lists of (index, value) pairs, you can compute the dot product by iterating through both lists simultaneously, similar to merging two sorted arrays. Only multiply values when indices match, and advance the pointer with the smaller index. This yields O(n + m) time where n and m are the number of non-zero elements, and O(1) extra space.

Pro tip: Mention that this approach is essentially a merge join on sorted keys, and highlight that it avoids iterating over the full length, which is crucial for scalability. Also note that if one list is much smaller, you could binary search each element of the smaller list in the larger one for O(n log m) time, but the two-pointer method is optimal for general sparse cases.

1. Clarify the representation

Confirm that each vector is given as a sorted list of (index, value) pairs, where indices are unique and in ascending order, and values are non-zero. This ensures we can use a merge-like traversal.

2. Initialize pointers and result

Set two pointers, i and j, to the start of each list, and initialize a variable dot_product to 0.

3. Traverse both lists simultaneously

While both pointers are within bounds, compare the indices at i and j. If they are equal, multiply the values and add to dot_product, then advance both pointers. If one index is smaller, advance only that pointer.

4. Handle remaining elements

Once one list is exhausted, stop because any remaining elements in the other list have indices that cannot match any in the exhausted list (since indices are unique and sorted).

5. Return the result and analyze complexity

Return dot_product. State that time complexity is O(n + m) where n and m are the number of non-zero elements, and space complexity is O(1) beyond the input.

Key Points to Mention

Sparse vector representation: only non-zero elements stored as (index, value) pairs, sorted by index.
Two-pointer technique similar to merging two sorted arrays, comparing indices to find matches.
Time complexity O(n + m) where n and m are the number of non-zero elements, which is optimal for this representation.
Space complexity O(1) extra space, as we only use a few pointers and a result variable.
Edge cases: empty lists, no matching indices, all indices match, one list much larger than the other.
Alternative approach: binary search for each element of the smaller list in the larger list, giving O(n log m) time, but two-pointer is better when both are similarly sized.

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