> Markdown version of [/jobs/ext/3716075-machine-learning-scientist-5-ads-bidding](https://www.wearedevelopers.com/jobs/ext/3716075-machine-learning-scientist-5-ads-bidding). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Scientist 5 - Ads Bidding - **Company:** Netflix, Inc. - **Location:** Los Gatos, United States (Remote available) - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Data Analysis, Python (Programming Language), Machine Learning, Operational Databases, Information Technology - **Published:** October 13, 2026 - **Apply:** https://startup.jobs/machine-learning-scientist-5-ads-bidding-netflix-com-10432630 ## About the Role * Advanced degree (PhD or Master's) in Computer Science, Statistics, Mathematics, or related quantitative field. * Proficiency in Python, Scala or Java. * Deep knowledge of machine learning, optimization, and data analysis techniques. * Experience with prototyping and deploying algorithms using large-scale production data. * Strong business acumen and ability to translate technical results into business impact. * Experience in building bidding algorithms. * Excellent communication and collaboration skills. ## Description * 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 auction and pricing mechanism design, ensuring alignment between bidding algorithms, marketplace efficiency, and business goals. * Partner closely with the product team to define bidding objectives, constraints, and trade-offs that align with product and revenue goals. * Communicate technical decisions, trade-offs, and experiment results to both technical and non-technical stakeholders, driving understanding and adoption of ML-driven bidding solutions.