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Design comment-likelihood prediction platform

RedditSoftware Engineer · Onsite
MediumSoftware EngineerOnsiteML System Design

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This question evaluates a candidate's competency in ML system design and infrastructure, covering feature pipelines, feature store strategy, training/serving consistency, online inference at high QPS, and production monitoring.

Scenario

You’re building an ML platform component that serves a model to predict the likelihood that a user will comment on a given post.

The interviewer says you can treat the model as a black box (you don’t need to pick a specific architecture); focus on ML infrastructure: feature pipelines, feature store, training/serving, inference, and production concerns.

Goals

Design an end-to-end system that supports:

  • Offline training data generation and model training
  • Online inference (real-time scoring) for product surfaces (e.g., feed ranking, notification candidate scoring)
  • A feature store strategy (offline + online)
  • Monitoring, logging, and iteration

Requirements & constraints (you may make reasonable assumptions)

  • High QPS online scoring (potentially tens of thousands/sec)
  • P95 latency budget for scoring: e.g., 50–150 ms end-to-end (state your assumption)
  • Freshness: some features need near-real-time updates (seconds to minutes)
  • Avoid training/serving feature skew
  • Handle cold start (new users/posts)
  • Support A/B testing and safe rollout

Deliverables

Explain:

  1. Data sources and event logging
  2. Feature engineering and feature store design
  3. Training pipeline and dataset versioning
  4. Online inference architecture (batch vs real-time, caching, fallbacks)
  5. Monitoring (data + model), retraining triggers, and reliability considerations