Applied intuition·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Interviewed for a software engineer role at Applied Intuition and got a classic LRU-style problem dressed up as a task scheduler. Pretty standard for this type of company but the variants they mentioned kept it from being totally autopilot.

Questions Asked (1)

Design and implement a task scheduler that manages a fixed-capacity pool of tasks, supporting scheduling new tasks and accessing existing ones, with least-recently-used eviction when the pool is full.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

Basically LC 146 with a costume on.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: fixed capacity, LRU eviction, and operations for scheduling and accessing tasks. Then propose a design using a hash map for O(1) access and a doubly linked list to track recency, ensuring O(1) eviction and updates. Discuss trade-offs like thread safety, persistence, and alternative data structures.

Pro tip: Mention that you would use a combination of a hash map and a doubly linked list to achieve O(1) for all operations, and proactively discuss how you would handle concurrency if the scheduler is used in a multi-threaded environment.

1. Clarify Requirements

Ask about expected operations, capacity limits, eviction policy details, and any concurrency or persistence requirements. Confirm that LRU eviction is strictly based on access time.

2. Choose Data Structures

Select a hash map for O(1) task lookup and a doubly linked list to maintain recency order. Explain how they work together to support O(1) insertion, access, and eviction.

3. Define Operations

Outline the algorithms for scheduling a new task (add to map and list, evict if full) and accessing a task (move to front of list). Ensure edge cases like updating an existing task are handled.

4. Address Concurrency and Scalability

Discuss thread safety using locks or concurrent data structures, and consider distributed scenarios if relevant. Mention potential bottlenecks and how to mitigate them.

5. Analyze Trade-offs

Compare your design with alternatives (e.g., using a priority queue or timestamp-based eviction) and explain why your approach is optimal for the given constraints.

Key Points to Mention

O(1) time complexity for scheduling, accessing, and evicting tasks using a hash map and doubly linked list.
Handling of edge cases: updating an existing task, evicting the least recently used when capacity is reached.
Thread safety considerations: using locks, read-write locks, or concurrent collections.
Memory management: ensuring no memory leaks when tasks are evicted.
Alternative eviction policies (e.g., LFU, FIFO) and why LRU is suitable here.
Potential extensions: persistence, distributed caching, or time-based expiration.

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