Netflix·Machine Learning Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Netflix ML Engineer screen, got a word frequency counter problem which felt more like a warmup than a real challenge. Clean enough problem but I kept second-guessing my design choices out loud which probably didn't help.

Questions Asked (1)

Design and implement a class that tracks word frequencies from a continuous stream of text, supporting methods to add text, retrieve the count for a specific word, and return all word counts.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

Went with a dictionary under the hood, nothing fancy.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements: expected stream volume, latency needs, and whether counts must be exact or approximate. Then propose a hash map-based solution with thread-safe operations, and discuss scaling strategies like sharding or probabilistic data structures for high-throughput scenarios.

Pro tip: Mention that in a real-time ML pipeline at Netflix, word frequencies often feed into feature stores, so you'd consider windowed counts and decay to capture trends, not just cumulative totals.

1. Clarify Requirements

Ask about data volume, latency, concurrency, and accuracy needs to determine if a simple hash map suffices or if distributed/approximate solutions are needed.

2. Design Core Data Structure

Propose a hash map (dictionary) mapping words to counts, with thread-safe access via locks or concurrent data structures for multi-threaded streams.

3. Implement Methods

Define addText to tokenize and update counts, getCount to retrieve a word's frequency, and getAllCounts to return the map or a snapshot.

4. Address Scalability

Discuss sharding by word hash, using distributed caches like Redis, or approximate structures like Count-Min Sketch for high-throughput streams.

5. Optimize and Trade-offs

Weigh memory vs. accuracy, consider concurrency bottlenecks, and mention techniques like batching updates or using read-write locks.

Key Points to Mention

Hash map for O(1) average time complexity for add and get operations
Thread safety: synchronized methods, ConcurrentHashMap, or lock striping
Tokenization: handling punctuation, case sensitivity, and Unicode
Scalability: sharding, distributed counters, or approximate algorithms (Count-Min Sketch)
Memory management: eviction policies for unbounded streams (LRU cache)
Real-time ML integration: windowed counts, decay, and feature extraction

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