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Figma·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

SeniorPrefer not to say
Jun 2026

Summary

Figma ML engineer interview that was more systems-heavy than I expected. The whole session centered on one design problem about undo behavior in a layer editor, and they kept pushing on edge cases I hadn't thought through.

Questions Asked (1)

Q1

Design a batch operation primitive for a layer/document editor where multiple mutations can be grouped and treated as a single atomic unit for undo. How do you structure the operation log, handle nested batches, and deal with partial failures mid-batch?

System DesignTechnical Trade-offsAlgorithms & Data Structures
Author's notes

Started with the basic command pattern and felt pretty good about it, but then they asked about nested batches and I kind of stalled.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints of the batch operation primitive, such as atomicity, nesting, and failure handling. Then propose a design using a command pattern with a hierarchical operation log, explaining how to manage nested batches and ensure atomicity through transactions or rollback mechanisms. Finally, discuss trade-offs and edge cases, including partial failures and performance considerations.

Pro tip: Emphasize the importance of idempotency and deterministic replay in undo/redo systems, as this demonstrates deep understanding of state management and reliability. Also, relate the design to ML engineering by mentioning how batch operations can optimize model updates or data processing pipelines.

1. Clarify Requirements and Constraints

Ask questions to understand the scope: What operations are included? How deep can nesting go? What are the performance and memory constraints? This ensures the design meets the actual needs.

2. Design the Operation Log Structure

Propose a hierarchical structure where each batch is a node containing a list of operations or sub-batches. Use a command pattern to encapsulate mutations, and maintain a stack for undo/redo.

3. Handle Nested Batches

Explain that nested batches are represented as child nodes in the hierarchy. When undoing, the entire top-level batch is undone atomically, including all nested operations. Use a depth counter or stack to track nesting.

4. Manage Partial Failures and Atomicity

Implement a transaction-like mechanism: either all operations in a batch succeed or none are applied. On failure, roll back to the state before the batch began. Use a two-phase commit or savepoints for nested batches.

5. Discuss Trade-offs and Optimizations

Consider trade-offs between memory usage and performance, such as compressing logs or using incremental snapshots. Mention how to handle concurrent edits and ensure consistency.

Key Points to Mention

  • Command pattern for encapsulating mutations
  • Hierarchical operation log with parent-child relationships
  • Atomicity via transactions or rollback mechanisms
  • Nested batch handling with depth tracking
  • Partial failure recovery using savepoints or two-phase commit
  • Trade-offs: memory vs. performance, compression, concurrency

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