Product Context

PayBridge serves small and medium-sized businesses through a payments dashboard and mobile app. The company wants to rank financing and payment-related offers—such as instant payout, working capital advances, card readers, invoicing tools, and cross-border payment discounts—so each merchant sees the most relevant offer at the right time.

Scale

Offer impressions happen on the home dashboard, checkout settings page, and email/push follow-up flows. Some offers have immediate feedback (click, apply), while others have delayed outcomes (approval, activation, repayment quality) that may arrive days later.

Task

Design an end-to-end ML system for personalized offer recommendation and ranking. Address the following:

1. Clarify the product objective, target users, and success metrics for ranking SMB payment offers.
2. Propose a multi-stage architecture for retrieval, ranking, and re-ranking, including what runs online vs batch.
3. Define the training data, labels, features, and model choices for each stage, including how you handle delayed feedback and cold start.
4. Design the serving stack with realistic latency and throughput assumptions, including feature storage, caching, and fallbacks.
5. Explain how you would evaluate the system offline and online, and how you would monitor drift, training-serving skew, and business-policy violations.
6. Identify key failure modes, especially around compliance, stale eligibility, and over-targeting merchants with low-quality offers.

Constraints

• Offers are subject to merchant eligibility, geography, underwriting, and compliance rules that must be enforced before ranking.
• Some labels are sparse and delayed by 7-30 days.
• The system must support new merchants with little history and new offers with limited interaction data.
• Cost matters: GPU-heavy online scoring is discouraged unless clearly justified.
• Explanations are needed for internal review on why a merchant received an offer.