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Machine Learning Scientist 5 - Ads Bidding

Netflix - USA - Remote | Los Angeles,California,United States of America | Los Gatos,California,United States of America

Posted May 20, 2026

Benefits

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Not verified last checked Jun 13, 2026
Salary
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401(k) match
Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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Schedule

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About this role

Machine Learning Scientist 5 - Ads Bidding USA - Remote | Los Angeles,California,United States of America | Los Gatos,California,United States of America At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next. We launched a new ad-supported tier in November 2022 and are building an in-house world-class ad tech ecosystem to offer our members more choices in consuming their content. Our new tier allows us to attract new members at a lower price point while also creating a compelling path for advertisers to reach deeply engaged audiences. Our Team: The Core Ads Algorithms team is the engine behind the intelligence that powers ad personalization at Netflix. Our mission is to develop innovative, data-driven solutions that deliver highly relevant ad experiences to our members and drive impactful results for advertisers-all while maintaining the exceptional quality and personalization that define the Netflix experience. Responsibilities: Design and implement machine learning-driven bidding algorithms that optimize ad performance against objectives such as Clicks, Conversions, CPA and ROAS,. Build, train, and evaluate bidding algorithms on large-scale production data, ensuring robustness to marketplace dynamics, seasonality, and distribution shifts. Develop online and offline evaluation frameworks to rigorously measure the impact of bidding algorithms and policy changes. Inform and influence

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