Netflix
Senior
Build low-latency ML systems powering real-time ad decisioning for Netflix's ad tier
A senior Machine Learning Engineer role on Netflix's Ads Platform Engineering org, spanning teams like Core Ads Serving, Inventory Management & Forecasting, Identity & Audiences, and Ads Programmatic. You'll build end-to-end ML model deployment and inference infrastructure for low-latency real-time ad systems, including yield optimization, bid ranking, pacing/dynamic allocation, and goal-based delivery (CPC/CPV/CPCV) models at Netflix's Big Data scale. The senior title (ML Engineer 5) and $466K-$750K comp range signal a high bar for production ML systems ownership in advertising.
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What this interview tests
- End-to-end ML model deployment/inference infra for low-latency ad serving
- Yield optimization, bid ranking, and dynamic allocation modeling
- Goal-based delivery optimization (CPC/CPV/CPCV)
- Large-scale data processing with Spark
- High-autonomy ownership and cross-functional delivery (Netflix culture)
Common question themes
Design a low-latency real-time ad decisioning/inference system
Build a yield optimization or bid ranking model — walk through the approach
How would you productionize a predictive model for campaign forecasting (impressions/reach/ROI)
Describe handling large-scale data with Spark for an ML pipeline
Netflix culture/freedom-and-responsibility behavioral: driving an ambiguous project independently
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