Senior ML Engineer, Ads Platform (Remote)

Bazeta; United States; Remote; USD 466,000 - 750,000; Full time

Job summary

Netflix in the United States is seeking a Machine Learning Engineer 5 for Ads Platform Engineering. You will join a team building high-performance ML models and real-time ad systems across client and server components to monetize content while maintaining user experience.

The role emphasizes low-latency inference, large-scale data processing with Spark, and collaboration across science, product, and platform teams to productionize models and optimize yield and CPC/CPV strategies.

Qualifications

• Proficiency in Java, C++, Python, or Scala with multi-threading experience.
• Experience building end-to-end ML model deployment and inference infra for low-latency real-time ad systems.
• Experience in handling data at extremely large volumes with Spark.
• Yield optimization, bidding models, and dynamic allocation of direct/programmatic inventory.
• Productionized predictive models to forecast ad campaigns (impressions, reach, clicks, conversions, ROI).

Responsibilities

• Build end-to-end ML deployment and inference infra for low-latency ads.
• Develop models for goal-based delivery optimization (CPC, CPV, CPM).
• Create scalable simulation for inventory scenarios and pricing strategies.
• Collaborate with cross-functional teams to productionize models at scale.
• Focus on advertiser outcomes while preserving user experience.

Skills

Java

C++

Python

Scala

Multithreading

Memory management

ML deployment infra

Low-latency systems

Model inference

Tools

Spark

Job description

Netflix in the United States is seeking a Machine Learning Engineer 5 for Ads Platform Engineering. You will join a team building high-performance ML models and real-time ad systems across client and server components to monetize content while maintaining user experience.

The role emphasizes low-latency inference, large-scale data processing with Spark, and collaboration across science, product, and platform teams to productionize models and optimize yield and CPC/CPV strategies.