← fireworks ai Interview Insights

fireworks ai·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Interviewed for a software engineer role at Fireworks AI and got a problem about maintaining a live set of merged busy intervals on a GPU. The core challenge was making insertion efficient while also supporting idle time queries. Pretty meaty for a single coding round.

Questions Asked (1)

Q1

Design a data structure that tracks busy time intervals for a GPU as tasks arrive in a stream. Each task adds a busy interval, and you need to support efficient insertion with merging of overlapping or adjacent intervals, plus a query that returns the current idle intervals. Target O(log n) insertion using a sorted structure.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I knew the merge intervals problem but doing it online, as a stream, is a different beast.

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AI HintsAI Generated

Suggested Approach

Propose a balanced BST (e.g., red-black tree) or skip list keyed by interval start, storing disjoint merged intervals. For insertion, locate the position, merge with overlapping/adjacent neighbors, and delete merged intervals; for idle query, traverse the tree to find gaps between consecutive intervals. Analyze time complexity and discuss trade-offs.

Pro tip: Mention that using a balanced BST with parent pointers allows O(log n) deletion of merged intervals, and that idle intervals can be derived on-the-fly without extra storage, showing awareness of memory and performance trade-offs.

1. Clarify requirements and assumptions

Ask about interval semantics (inclusive/exclusive), stream characteristics, concurrency needs, and whether idle intervals should be returned as a list or iterator.

2. Choose data structure and justify

Select a balanced BST (e.g., red-black tree) or skip list to maintain disjoint busy intervals sorted by start time, ensuring O(log n) search, insert, and delete.

3. Design insertion with merging

Find the insertion point, then merge with overlapping or adjacent intervals by deleting them and inserting the combined interval; handle edge cases like merging multiple intervals.

4. Design idle interval query

Traverse the tree in order, computing gaps between consecutive busy intervals and from boundaries (e.g., 0 to first start, last end to infinity).

5. Analyze complexity and trade-offs

State O(log n) insertion and O(k) idle query (k = number of idle intervals), and discuss alternatives like interval trees or augmented structures.

Key Points to Mention

  • Use of a balanced BST (e.g., red-black tree) or skip list for O(log n) operations.
  • Merging strategy: delete overlapping/adjacent intervals and insert the union.
  • Idle intervals computed as gaps between consecutive busy intervals, including boundaries.
  • Time complexity: O(log n) insertion, O(k) idle query where k is number of idle intervals.
  • Space complexity: O(n) for n disjoint busy intervals.
  • Trade-offs: alternative data structures (interval trees, segment trees) and their pros/cons.

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