Perplexity AI·Software Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

Perplexity AI coding round for a software engineer role. One meaty design question that looked manageable at first glance but had enough edge cases to keep you busy for a full session.

Questions Asked (1)

Design a CreditTracker class with methods to add credit (with a start and end time), subtract credit (at a given time, deducting from the earliest-expiring credit first), and check the current credit balance at a given time. Calls to add and subtract can arrive out of chronological order. Walk through your data structure choices, implementation, and time/space complexity.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The greedy deduction rule (earliest expiry first) is what makes this non-trivial.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a data structure that supports efficient insertion, deletion, and querying of credits with expiration times. Walk through the implementation of add, subtract, and balance methods, emphasizing how to handle out-of-order operations. Finally, analyze the time and space complexity of each operation and discuss potential optimizations.

Pro tip: Mention that you would use a balanced BST or a priority queue with lazy deletion to efficiently find the earliest-expiring credit, and discuss how to handle out-of-order operations by storing credits in a time-ordered structure. Also, consider edge cases like subtracting more credit than available or adding credits with overlapping time ranges.

1. Clarify Requirements and Constraints

Ask questions to understand the expected scale (number of operations, time range), whether credits can have overlapping validity periods, and if negative balances are allowed. Clarify the exact semantics of 'current credit balance at a given time'.

2. Choose Data Structures

Propose using a balanced binary search tree (e.g., TreeMap in Java) keyed by expiration time to store credits, allowing efficient retrieval of the earliest-expiring credit. Alternatively, consider a min-heap with lazy deletion for subtract operations.

3. Implement add and subtract Methods

For add, insert the credit into the BST with its expiration time as key. For subtract, repeatedly remove or reduce the earliest-expiring credit until the requested amount is deducted, handling cases where credits expire before the given time.

4. Implement balance Query

To compute the balance at a given time, sum all credits that are valid at that time. If using a BST, this may require iterating over all credits or maintaining an auxiliary structure for efficient range queries.

5. Analyze Complexity and Discuss Trade-offs

Analyze time complexity: add O(log n), subtract O(k log n) where k is number of credits consumed, balance O(n) or O(log n) with augmented tree. Discuss space complexity O(n). Mention potential optimizations like using a segment tree or Fenwick tree for balance queries.

Key Points to Mention

Use of a balanced BST (e.g., TreeMap) or priority queue to efficiently find the earliest-expiring credit.
Handling out-of-order operations by storing credits with their start and end times and only considering those valid at the query time.
Lazy deletion or marking credits as expired to avoid frequent restructuring.
Edge cases: subtracting more than available, credits expiring before subtraction time, overlapping credits.
Time complexity: O(log n) for add, O(k log n) for subtract, O(n) or O(log n) for balance with augmented data structures.
Space complexity: O(n) for storing all credits.

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