Case Study: Notification Timing & Content Optimization
Walk through a full ML system design interview answer for an RL-based send/delay/suppress policy
Model interview answer for designing a reinforcement-learning system that decides what and when to send in a messaging product, optimizing for long-term retention instead of short-term opens: state, action and reward design that avoids reward hacking, a bandit-to-RL model progression, a mandatory off-policy evaluation gate before launch, production guardrails against fatigue, and an experiment design that measures retention rather than open rate.
Practice questions (5)
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Diagnose a Reward-Hacked Notification Policy
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When to Graduate from a Contextual Bandit to Full RL
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An Off-Policy Evaluation Estimate With a Wide Confidence Interval
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Design the Guardrail Layer for the Notification Policy
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Design the Launch Experiment for the RL Notification Policy
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