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Perplexity·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say
May 2026

Summary

Interviewed for a software engineering role at Perplexity and got a pretty meaty coding problem centered on an in-memory task management system. The core challenge was extending it with dependency tracking and a cascade-failure mechanism, which sounds manageable until you're actually tracing through a DAG under pressure.

Questions Asked (1)

Q1

You're given an in-memory Todo List system with basic CRUD operations. Extend it to support dependency tracking between tasks (parent/child relationships), then implement a cascade-failure function: when a task is marked failed, all tasks that transitively depend on it must also be marked failed. Handle performance for large, deeply interconnected graphs.

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

I started with the dependency modeling, which felt fine, just adding parent and child sets to each task node.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a graph-based data model with adjacency lists for parent-child relationships. Explain the cascade-failure algorithm using BFS/DFS with visited set to avoid cycles, and discuss performance optimizations for large graphs. Finally, address trade-offs between memory, latency, and consistency.

Pro tip: Mention that you would use an iterative BFS instead of recursion to avoid stack overflow on deep graphs, and consider batch updates or lazy propagation if the graph is extremely large.

1. Clarify Requirements and Constraints

Ask about expected graph size, read/write patterns, consistency requirements, and whether cycles are possible. Confirm that failure propagation is transitive and that tasks can have multiple parents/children.

2. Design Data Model

Propose storing tasks in a hash map (id -> task) and maintaining adjacency lists for dependencies (children and parents). Consider bidirectional links for efficient traversal in both directions.

3. Implement Cascade Failure

Use BFS or DFS from the failed task, traversing children and marking them failed. Maintain a visited set to handle cycles and avoid redundant work. Update task statuses atomically if needed.

4. Optimize for Performance

Analyze time and space complexity (O(V+E)). Discuss optimizations: iterative traversal, early termination if already failed, batch updates, and possibly lazy propagation or incremental updates for very large graphs.

5. Discuss Trade-offs and Extensions

Compare eager vs lazy propagation, memory overhead of bidirectional links, and consistency vs latency. Mention potential concurrency issues and how to handle them (e.g., locking, versioning).

Key Points to Mention

  • Graph representation: adjacency list vs adjacency matrix, and why adjacency list is better for sparse graphs.
  • Cycle detection and handling: using visited set to avoid infinite loops.
  • Time and space complexity: O(V+E) for traversal, O(V+E) space for graph storage.
  • Iterative BFS/DFS to avoid recursion depth limits.
  • Batch updates or lazy propagation for performance with large graphs.
  • Concurrency and consistency: locking, transactional updates, or eventual consistency.

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