Cognitiv·Machine Learning Engineer·Onsite - System Design / Architecture
Jun 2026
System design round at Cognitiv for an MLE role. The whole thing was focused on a feature store for ads/recommendation ranking, which sounds scoped until you realize they want you to cover online serving, offline training, streaming ingestion, backfills, and monitoring all in one go.
- Design a real-time feature store for ML systems used in ads or recommendation ranking, supporting both low-latency online inference and offline training with point-in-time correctness.
- What APIs and abstractions should a feature store expose to its consumers?
- How would you keep online and offline feature pipelines consistent with each other?
- How do you handle fresh streaming features, backfills for historical data, and late-arriving events?
- How would you scale the feature store and monitor data quality and reliability?
“This is the kind of question where you think you have a plan and then five minutes in you realize you've been drawing boxes without actually answering anything.” The rest of the author's notes on Machine Learning Engineer interview at Cognitiv, Onsite - System Design / Architecture round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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