Weride·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Weride coding interview, got a matrix multiplication problem that felt more like a systems/math warmup than a pure algo question. Pretty straightforward if you know your linear algebra basics, but they pushed on edge cases and complexity in ways I didn't fully anticipate.

Questions Asked (1)

Implement matrix multiplication for two matrices A (m x n) and B (n x p), returning the product C (m x p). Include dimension validation and explain the time complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

I started with the triple nested loop and got that right pretty fast.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then present a clean implementation with dimension validation. After coding, analyze the time complexity and discuss potential optimizations like blocking or using libraries.

Pro tip: Mention that in production, you'd use optimized libraries like BLAS, but for interviews, focus on the core algorithm and trade-offs. Also, discuss cache efficiency and parallelization opportunities.

1. Clarify requirements and edge cases

Ask about matrix dimensions, data types, and whether the matrices are dense or sparse. Discuss handling of empty matrices and invalid dimensions.

2. Design the algorithm

Explain the standard triple-loop approach: for each row i of A and column j of B, compute dot product of A[i][k] and B[k][j]. Validate that A's columns equal B's rows.

3. Implement with validation

Write code that first checks if A's column count equals B's row count, returning an error if not. Then initialize C with zeros and fill it using nested loops.

4. Analyze complexity

State that time complexity is O(m * n * p) and space complexity is O(m * p) for the output. Mention that this is optimal for the naive algorithm.

5. Discuss optimizations and trade-offs

Mention cache-friendly loop ordering (i, k, j), blocking for better cache use, and parallelization. Also note that specialized libraries (e.g., BLAS) are used in practice.

Key Points to Mention

Dimension validation: A's columns must equal B's rows.
Time complexity: O(m * n * p) for naive multiplication.
Space complexity: O(m * p) for the result matrix.
Loop ordering for cache efficiency (e.g., i-k-j order).
Potential optimizations: blocking, parallelization, using optimized libraries.
Handling edge cases: empty matrices, zero dimensions.

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