← Microsoft Interview Insights

Microsoft·AI Engineer·Technical Phone Screen·Senior

Senior
May 2026

Summary

Microsoft AI Engineer interview with a meaty coding problem around LLM training infrastructure. Single question but it went pretty deep into both algorithms and system design territory, which I wasn't fully expecting.

Questions Asked (1)

Q1

Implement sample packing for supervised fine-tuning of an LLM. Given a list of (prompt_tokens, answer_tokens) pairs and a target sequence length, return the packed sequences, attention masks, and loss masks. Your solution should handle block-diagonal attention so tokens across different packed examples can't attend to each other, track which positions contribute to the loss, and use a bin-packing heuristic to minimize padding.

System DesignAlgorithms & Data StructuresTechnical Trade-offs
Author's notes

This one had more layers than I initially clocked.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then outline a bin-packing strategy to group examples into sequences of target length. Explain how to construct the packed token IDs, block-diagonal attention masks, and loss masks, and discuss trade-offs between packing efficiency and implementation complexity.

Pro tip: Mention that block-diagonal attention can be efficiently implemented using FlashAttention's varlen API or by creating a 2D mask from segment IDs, and that loss masks should exclude prompt tokens and padding to avoid contaminating the gradient.

1. Clarify Requirements and Constraints

Confirm the target sequence length, whether padding is allowed, and how to handle examples longer than the target. Discuss the importance of minimizing padding for efficiency.

2. Design Bin-Packing Heuristic

Propose a greedy first-fit decreasing (FFD) or best-fit decreasing (BFD) algorithm to pack examples into bins of capacity equal to the target length. Explain how to sort examples by total length to improve packing.

3. Construct Packed Sequences and Masks

For each bin, concatenate token IDs, create an attention mask that is block-diagonal (1s within each example, 0s across examples and padding), and a loss mask that is 1 only for answer tokens (excluding prompt and padding).

4. Implement Efficient Attention

Describe how to use the block-diagonal mask with standard attention or leverage optimized kernels like FlashAttention with variable-length sequences to avoid materializing the full mask.

5. Discuss Trade-offs and Edge Cases

Address trade-offs between packing efficiency and computational overhead, handling of examples longer than target length (e.g., truncation or splitting), and the impact on training dynamics.

Key Points to Mention

  • Bin-packing heuristic (e.g., first-fit decreasing) to minimize padding and maximize GPU utilization.
  • Block-diagonal attention mask construction using segment IDs or cumulative lengths to prevent cross-example attention.
  • Loss mask that selects only answer tokens (not prompt or padding) for supervised fine-tuning.
  • Efficient implementation using FlashAttention's varlen API or similar to avoid O(n^2) mask materialization.
  • Handling of examples longer than target length: truncation, splitting, or skipping.
  • Trade-offs: packing increases sequence length but reduces padding; block-diagonal attention adds masking overhead but is necessary for correctness.

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