TikTok·Machine Learning Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

ML engineer interview at TikTok with a product design question focused on the recommendation system behind the For You page. Pretty open-ended, which I wasn't fully prepared for coming from a more traditional ML background.

Questions Asked (1)

How would you design the 'For You' recommendation feed on TikTok?

System DesignProduct Sense & IdeationTechnical Trade-offs

Author's notes

I went straight to the ML pipeline: candidate generation, ranking, re-ranking for diversity.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the product goals and constraints, then outline a high-level system architecture covering data collection, candidate generation, ranking, and serving. Dive into the ML components, emphasizing feature engineering, model choices, and online evaluation, while discussing trade-offs between relevance, diversity, and latency.

Pro tip: Emphasize the importance of real-time signals and the cold-start problem, and propose a multi-stage ranking system with a lightweight first stage to handle billions of videos. Show awareness of TikTok's unique emphasis on user engagement and content diversity.

1. Clarify Requirements and Goals

Ask questions to understand the scale, latency requirements, and key metrics (e.g., watch time, likes, shares). Confirm the focus on personalized recommendations for the 'For You' page.

2. High-Level System Architecture

Outline the main components: data ingestion (user interactions, video metadata), candidate generation, ranking, and serving. Mention offline and online processing pipelines.

3. Candidate Generation

Describe methods to retrieve a few thousand candidates from millions of videos, such as collaborative filtering, content-based filtering, and trending videos. Highlight the use of embeddings and approximate nearest neighbor search.

4. Ranking and Personalization

Explain the ranking model (e.g., deep neural networks) that predicts engagement probabilities. Discuss feature engineering (user, video, context features) and handling multiple objectives (e.g., watch time, likes).

5. Evaluation and Iteration

Cover offline metrics (AUC, recall@k) and online A/B testing. Discuss how to monitor and mitigate issues like feedback loops, filter bubbles, and cold start.

Key Points to Mention

Two-stage or multi-stage ranking architecture to balance scalability and accuracy.
Use of real-time features (e.g., recent watches, time of day) and streaming data for freshness.
Embedding-based retrieval for candidate generation and approximate nearest neighbor search.
Multi-task learning to optimize for multiple engagement signals simultaneously.
Handling cold start for new users and videos via content-based and demographic features.
Trade-offs between latency, relevance, diversity, and computational cost.

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