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ReflectionAI·Software Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Coding question for a Research Engineer role at ReflectionAI. The problem was a sequence transformation that sounds deceptively simple but pushes you toward thinking in a vectorized/tensor style rather than just looping through.

Questions Asked (1)

Q1

Given a binary 0/1 sequence, transform it so every 0 becomes -1 and every 1 is replaced by the index of the contiguous block of 1s it belongs to (blocks numbered left to right starting from 0). For example, `0010110111` becomes `-1,-1,0,-1,1,1,-1,2,2,2`. Implement this, preferably in a vectorized or tensor-style approach.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

My first instinct was a plain loop with a counter and I coded that up pretty fast, but the nudge toward vectorized solutions was clearly the real ask.

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AI HintsAI Generated

Suggested Approach

First clarify the problem and edge cases, then present a vectorized solution using cumulative sums and boundary detection to assign block indices. Discuss trade-offs between vectorized and iterative approaches, and analyze time/space complexity.

Pro tip: Mention that vectorized operations may not always be faster due to overhead; for small sequences, a simple loop can be more efficient. Also, consider memory usage if the sequence is large.

1. Clarify the problem

Restate the transformation rules and confirm understanding with examples. Ask about input format, size, and whether in-place modification is required.

2. Design the algorithm

Use vectorized operations: compute block starts and ends, then assign block indices using cumulative sums. For each position, if it's a 1, its value is the number of blocks started up to that point minus 1.

3. Implement the solution

Write code using array operations (e.g., NumPy or PyTorch). Handle edge cases like all zeros, all ones, and empty input.

4. Analyze complexity and trade-offs

Discuss O(n) time and space complexity. Compare vectorized vs iterative approaches in terms of readability, performance, and hardware utilization.

5. Test and validate

Walk through the example and additional test cases to ensure correctness. Mention potential pitfalls like off-by-one errors in block indexing.

Key Points to Mention

  • Vectorized operations using cumulative sums and boundary detection
  • Time and space complexity analysis (O(n) time, O(n) space)
  • Trade-offs between vectorized and iterative implementations
  • Edge cases: empty input, all zeros, all ones, single element
  • Use of libraries like NumPy or PyTorch for tensor-style operations
  • Potential performance considerations for large sequences

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