Tesla·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Apr 2026

Summary

Tesla ML engineer coding round, one problem the whole session. Felt pretty clean on the surface but there were a few moments where I second-guessed myself on the edge case handling.

Questions Asked (1)

Given a batch of 2D waypoint trajectories with shape [B, N, 2], compute for each batch item and each waypoint the remaining path length from that waypoint to the end of the trajectory. The output should have shape [B, N], where each value is the suffix sum of Euclidean distances between consecutive waypoints. Must run in O(B·N) time and handle edge cases like a single waypoint.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The suffix sum part clicked fast, you just iterate backwards and accumulate segment lengths.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem and edge cases, then propose an O(B·N) solution using a reverse cumulative sum of Euclidean distances. Explain the algorithm step-by-step, analyze time and space complexity, and discuss potential optimizations or trade-offs.

Pro tip: Mention that you can compute the distances and suffix sums in a single reverse pass, avoiding extra memory allocation for the distance array, which is crucial for large batches. Also, highlight that this operation is a common pattern in trajectory processing and can be vectorized efficiently on GPUs.

1. Clarify the problem and edge cases

Restate the problem to ensure understanding, and explicitly list edge cases such as N=1 (remaining length is 0) and N=0 (empty trajectory). Confirm that the output should be a tensor of shape [B, N] with the same data type as the input.

2. Propose an O(B·N) algorithm

Describe a two-pass approach: first compute the Euclidean distances between consecutive waypoints for each batch item, then compute the suffix sum of these distances in reverse order. Emphasize that both passes are O(B·N) and can be fused into a single reverse pass.

3. Detail the implementation

Explain how to compute distances using vectorized operations (e.g., torch.norm or manual sqrt of sum of squares) and how to perform the reverse cumulative sum efficiently. For example, initialize a running sum to 0, iterate from the last waypoint to the first, and store the running sum at each step.

4. Analyze complexity and trade-offs

State that time complexity is O(B·N) and space complexity is O(B·N) for the output (or O(1) extra if in-place). Discuss potential trade-offs: using a single reverse pass saves memory but may be less readable; using built-in functions like torch.cumsum is concise but may allocate extra memory.

5. Discuss optimizations and applications

Mention that the operation can be vectorized across the batch dimension and is GPU-friendly. Optionally, note that for very large N, a parallel scan (e.g., prefix sum) could be used, but a simple reverse loop is sufficient for typical sizes.

Key Points to Mention

Euclidean distance computation: sqrt((x2-x1)^2 + (y2-y1)^2)
Suffix sum (reverse cumulative sum) of distances
Edge case handling: N=1 yields 0, N=0 yields empty tensor
Time complexity O(B·N) and space complexity O(B·N) for output
Vectorization and GPU efficiency for batch processing
Potential memory optimization by fusing distance computation and suffix sum in one pass

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