Apple·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

Apple MLE interview that was basically one meaty system design problem about building a TTL cache from scratch. The question sounds simple but they really dig into the edge cases and follow-ups.

Questions Asked (1)

Design and implement an in-memory key-value cache where each entry has its own time-to-live (TTL). Include put, get, and optionally delete operations.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I jumped straight to the implementation and almost skipped the clock source discussion entirely.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: expected scale, concurrency needs, and whether TTL is per-entry and absolute or sliding. Then propose a design using a hash map for O(1) key lookup and a min-heap or time-ordered structure for efficient expiration, discussing trade-offs between eager and lazy expiration. Finally, outline the implementation details for put, get, and delete, including thread-safety and memory management.

Pro tip: Mention that in ML systems, caches often store model predictions or feature vectors, so TTLs might be set based on data freshness or model retraining cycles. Also, highlight that Apple values privacy and efficiency, so consider memory footprint and secure deletion.

1. Clarify Requirements and Constraints

Ask about expected number of entries, read/write ratio, concurrency, and whether TTL is absolute or sliding. Confirm if delete is needed and if eviction policies (e.g., LRU) are required when memory is full.

2. Choose Data Structures

Propose a hash map for key-value storage and a min-heap (priority queue) keyed by expiration time for efficient TTL management. Discuss alternatives like a time-wheel or sorted set, and trade-offs in time/space complexity.

3. Design Core Operations

Define put(key, value, ttl): insert into map and heap. get(key): check if expired; if so, remove and return null; else return value. delete(key): remove from both structures. Explain how to handle expiration lazily or with a background thread.

4. Address Concurrency and Memory

Discuss thread-safety using locks (e.g., fine-grained locking or read-write locks) and potential contention. Cover memory management: eviction when full, and cleanup of expired entries to avoid leaks.

5. Analyze Trade-offs and Optimizations

Compare eager vs. lazy expiration, and heap vs. other structures. Mention possible optimizations like batching expirations, using approximate TTLs, or leveraging existing libraries (e.g., Guava Cache).

Key Points to Mention

Time complexity: O(1) average for put/get/delete with hash map, O(log n) for heap operations on expiration.
Expiration strategies: lazy (on access) vs. eager (background thread), and their impact on latency and memory.
Concurrency: thread-safe implementation using locks or concurrent data structures, and handling race conditions.
Memory management: eviction policies (LRU, LFU) when capacity is reached, and periodic cleanup of expired entries.
ML relevance: caching model predictions or features with TTL based on data freshness or model version.
Apple-specific considerations: privacy (secure deletion), efficiency (low memory footprint), and integration with existing systems.

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