Netflix
Senior
Netflix Ads Decisioning & Optimization engineer — real-time ad ranking, bidding, and pacing infra with sub-20ms ML serving
A senior (7+ years) distributed systems role on Netflix Ads' Decisioning & Optimization team, building the real-time ad decisioning path — ranking, scoring, bidding, and pacing — plus ML model serving infrastructure supporting dozens of concurrent hot-path models at sub-20ms P99 inference. Requires genuine ad-tech domain experience (2+ years) alongside distributed systems depth.
走进这场面试
免费 · 一场按这个岗位校准的真语音模拟
这场面试考什么
- Real-time ad decisioning: ranking, scoring, bidding, pacing under strict latency/throughput SLAs
- ML model serving infrastructure at sub-20ms P99 (routing, fallback, calibration, lifecycle)
- Ad-tech domain fundamentals: inventory management, frequency capping, supply-demand
- Auction mechanics and budget pacing/delivery optimization
- Simulation/offline validation frameworks for marketplace changes
- Operational excellence: reliability, observability, incident response
常见提问方向
Design a real-time ad ranking/bidding system meeting strict P99 latency budgets
How would you build ML model serving infra supporting dozens of concurrent hot-path models with fallback tiers
Explain auction mechanics you've implemented or reasoned about: first-price vs second-price, reserve pricing, bid shading
How would you design a budget pacing system to keep campaign delivery accurate across a campaign's lifetime
Describe productionizing a data science model into a low-latency serving path
How would you build a simulation framework to validate marketplace changes before live rollout
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