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Instacart·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Instacart software engineer interview focused on a task management system, basically a mini CRUD + ranking problem. Pretty reasonable scope for a coding round, though the details around tie-breaking and edge cases were where things got interesting.

Questions Asked (1)

Q1

Design and implement a task management system with ADD_TASK, UPDATE_TASK, DELETE_TASK, GET_TASK, and TOP_K operations. ADD_TASK should return false if the task ID already exists, and TOP_K should return the top k tasks by priority with ties broken alphabetically by task ID.

Algorithms & Data StructuresSystem DesignData Modeling
Author's notes

The CRUD part was fine, just a dict keyed by task_id and you're mostly done.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a data structure that supports all operations efficiently. For TOP_K, consider a balanced BST or heap with lazy deletion, and explain how to handle ties by task ID. Walk through the implementation details and analyze time/space complexity.

Pro tip: Mention that you would use a hash map for O(1) task lookups and a balanced BST (or heap with lazy deletion) for priority ordering, and explicitly discuss how to break ties by task ID to avoid ambiguity.

1. Clarify Requirements

Ask about expected scale, operation frequency, and whether task priorities can be updated. Confirm that task IDs are unique strings and priorities are integers.

2. Choose Data Structures

Propose a hash map for O(1) task retrieval and a balanced BST (e.g., TreeMap) or heap for priority ordering. Explain how to handle ties by task ID.

3. Design Operations

Detail ADD_TASK (check existence, insert into both structures), UPDATE_TASK (update priority and reorder), DELETE_TASK (remove from both), GET_TASK (hash map lookup), and TOP_K (retrieve top k from BST or heap).

4. Analyze Complexity

Provide time and space complexity for each operation, highlighting O(1) for GET_TASK and O(log n) for ADD/UPDATE/DELETE, and O(k log n) or O(k) for TOP_K.

5. Discuss Trade-offs and Optimizations

Compare BST vs heap approaches, mention lazy deletion for heaps, and consider concurrency or persistence if relevant to the system design context.

Key Points to Mention

  • Use a hash map for O(1) task lookups by ID.
  • Use a balanced BST (e.g., TreeMap) or a heap with lazy deletion for priority ordering.
  • Define a comparator that orders by priority descending and then by task ID ascending for tie-breaking.
  • For UPDATE_TASK, remove the old entry and insert the updated one to maintain ordering.
  • For TOP_K, traverse the BST in-order or extract from heap, ensuring ties are broken correctly.
  • Analyze time complexity: O(1) for GET_TASK, O(log n) for ADD/UPDATE/DELETE, and O(k log n) or O(k) for TOP_K.

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