Own the real-time ad decisioning and optimization path — auction, ranking, pacing, and ML model serving at sub-20ms P99

Lead technical direction for Netflix Ads' Decisioning & Optimization team, owning the real-time ad decisioning path: multi-stage auction, ranking, scoring, bidding, and pacing under strict latency constraints. This is a 60% builder / 40% technical-leadership role scaling ML model serving infrastructure to dozens of concurrent hot-path models with sub-20ms P99 inference.

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What this interview tests

Real-time ad decisioning: auction, ranking, scoring, bidding, pacing
ML model serving infrastructure at sub-20ms P99 under high QPS
Budget pacing and goal-based delivery optimization
Technical leadership across multiple teams / architectural ownership
Simulation and offline validation frameworks for marketplace changes

Common question themes

Design a multi-stage, low-latency ad auction/ranking pipeline

Scaling ML model serving with fallback tiers and model routing under load

Building a real-time budget pacing / delivery optimization system

Earning trust and driving architecture as a hands-on technical leader

Auction mechanics and marketplace tradeoffs (fill, pricing, bid shading)

How candidates describe it

Real Distributed Systems Engineer 6 - Decisioning & Optimization interview stories — retold from candidates' public write-ups, with sources.

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