Build the real-time ML forecasting and simulation systems behind Netflix's ad inventory

Netflix's new Ads Inventory Management & Forecasting team is hiring a senior ML engineer to build real-time inventory forecasting models and high-performance ad-server simulations that power dynamic pricing, rate cards, and yield optimization. The role sits on a young team building foundational advertising infrastructure from scratch in the fast-growing Connected TV ads space.

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What this interview tests

Real-time ML model deployment and low-latency inference infra
Large-scale data processing with Spark
Ad campaign forecasting (impressions, reach, clicks, conversions, ROI)
Inventory/pricing simulation modeling
Publisher-side ad tech and yield optimization

Common question themes

Design a low-latency real-time ML inference system for ad inventory forecasting

How would you build a simulation engine to model pricing and demand-fluctuation scenarios

Describe productionizing a predictive model and how you validated it against real outcomes

Publisher-side yield optimization vs. demand-side bidding — what's different

Handling extremely large data volumes with Spark in a production ML pipeline

How candidates describe it

Real Machine Learning Engineer 5 interview stories — retold from candidates' public write-ups, with sources.

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