This role pays more than
99%
of similar roles. Most pay
$195,000–$259,212
— the shaded band above.
At the midpoint, this role pays about
$608k
versus about
$227k
for comparable roles.
Based on 240 similar postings.
Employer
About Netflix
Netflix is the world''s leading streaming entertainment service, offering a vast library of TV series, films, documentaries, and original content to subscribers in over 190 countries. Industry: Streaming Entertainment & Media
Netflix currently has
28 open roles
on FindRole.
Listed pay typically runs
$440,000–$750,000
across 27 roles with salary data.
Machine Learning Engineer 5 - Ads Inventory Management & Forecasting joins the Ads Platform Engineering team to build state-of-the-art real-time inventory forecasting solutions and systems for publisher inventory management. The role involves developing high-performance ad server simulations, implementing monetization strategies like dynamic pricing and yield optimization, and building infrastructure for end-to-end machine learning model deployment and inference in low-latency environments. Key responsibilities include productionizing predictive models to forecast campaign effectiveness metrics such as impressions, reach, and ROI while managing large volumes of data using Spark. The position requires expertise in the advertising marketplace, specifically regarding publisher-side challenges like fill rates and inventory allocation. Technical skills include experience with Lucene indexes, ad servers, bidders, and industry standards like VAST or OpenRTB to solve complex problems within the rapidly growing connected TV advertising space.
What does a Machine Learning Engineer earn in California?
Median $246394 from 172 postings across 28 companies.
Build end-to-end ML model deployment and inference infrastructure for low-latency real-time ad systems.
Process and manage extremely large volumes of data using big data tools like Spark.
Productionize predictive models to forecast campaign effectiveness metrics such as impressions, reach, and ROI.
Develop scalable simulation solutions to model inventory scenarios including demand fluctuations and pricing strategies.
Build systems for publisher inventory management, including dynamic pricing and yield optimization.
Implement technical solutions to ensure advertiser brand safety and relevant ad delivery for members.
Deploy and scale machine learning models in collaboration with cross-functional product and engineering teams.
What we're looking for
Experience in building end-to-end ML model deployment and inference infrastructure for low-latency real-time ad systems.
Experience handling large volumes of data using big data tools like Spark.
Experience productionizing predictive models to forecast advertising campaign effectiveness, including metrics like impressions, reach, clicks, conversions, and ROI.
Experience building scalable simulation solutions to model inventory scenarios, demand fluctuations, pricing strategies, and allocation.
General understanding of the advertising marketplace with a focus on publisher-side challenges like fill rates and revenue maximization.
Ability to collaborate with cross-functional stakeholders including science, product, engineering, operations, design, and consumer research teams.
Familiarity with Lucene index construction for large volumes of data.
Knowledge of publisher-side ad tech systems, yield optimization, and industry standards like VAST or OpenRTB.