Netflix
Senior
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)
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