Design email ranking and summarization in Outlook
Overview
This question evaluates proficiency in designing end-to-end machine learning systems for personalized email ranking and abstractive summarization, encompassing personalization, debiasing and exposure-control methods, feedback-loop management, privacy-compliant data handling, low-latency serving, and safety controls for generated text.
Microsoft
Jan 6, 2026, 12:00 AM
Machine Learning Engineer
Onsite
ML System Design
0
0
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Scenario
You are building ML features for an email client:
- Debias email display order (inbox ranking) so users see the most relevant emails first while avoiding systematic biases (e.g., always ranking certain senders/threads too high due to historical exposure).
- Personalize email priority and summary (e.g., “Important”, “Needs reply”, short digest).
Task
Propose an end-to-end system design covering ranking + summarization.
Requirements
- Personalized per user; works for cold-start users.
- Must handle feedback loops and exposure bias.
- Privacy and compliance constraints (PII in email content).
- Low latency for inbox rendering.
What to cover
- Data and labels (implicit/explicit signals).
- Model choices for ranking and summarization.
- Debiasing approach (counterfactual learning, exploration).
- Evaluation and metrics (offline + online).
- Safety/quality for summaries (hallucination control, sensitive data).
Solution
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