What are the responsibilities and job description for the Senior Machine Learning Engineer - Ads Bidding & Pacing position at Apple, Inc.?
At Apple, we focus deeply on our customers' experience. Apple Ads brings this same approach to advertising, helping people find exactly what they're looking for and helping advertisers grow their businesses. Our technology powers ads and sponsorships across Apple Services, including the App Store, Apple News, and MLS Season Pass. \\n\\nEverything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes-from small app developers to big, global brands. Because when advertising is done right, it benefits everyone.\\n\\nWe are seeking a self-motivated individual that will build out the next generation of our ads platforms and ensure that Apple provides the most relevant and high quality ads experience while maintaining a healthy marketplace. You should have experience developing and implementing machine learning or optimization algorithms, ideally within the ads space, recommendations, or search relevance. You will have an excellent understanding of scalable architectures and thrive working in Agile environments.
In this role, you will design and build scalable solutions that enable advertisers to optimize for their campaign goals and performance on the Apple Ads. You will have the opportunity to build the next generation solutions for budget and bid optimization that enable driving optimal campaign performance and advertiser experience. You will have the opportunity to apply your ability to move the state of the art techniques in a fast growing business that positively impacts publishers, developers and Apple users at global scale. The ability to be a great teammate under tight deadline constraints is key to success.
5 years of experience building machine learning and quantitative optimization capabilities across many different product areas at scale \nExperience in machine learning, quantitative methods, control systems, or reinforcement learning \nAbility to apply and implement research concepts, ultimately in production quality code\nExperience defining clear, testable research hypotheses, including intended impact on the business\nDeep knowledge of design of experiments, online experimentation approaches, preferably at scale\nAbility to formulate and advocate for R&D objectives and results to cross-functional team members including executive business leadership and product management\nExperience contributing and/or reviewing research for top conferences and publications\nDeep fluency in Java or Python.\nExperience with Spark, Hadoop or other distributed frameworks.\nPhD in Machine Learning, Statistics, Control Theory, Forecasting, Optimization, Reinforcement Learning or related field with experience building production systems or have equivalent experience working with large data science / machine learning projects in industry.
Experience in ads optimization, recommendations, or search relevance optimization is highly preferred
In this role, you will design and build scalable solutions that enable advertisers to optimize for their campaign goals and performance on the Apple Ads. You will have the opportunity to build the next generation solutions for budget and bid optimization that enable driving optimal campaign performance and advertiser experience. You will have the opportunity to apply your ability to move the state of the art techniques in a fast growing business that positively impacts publishers, developers and Apple users at global scale. The ability to be a great teammate under tight deadline constraints is key to success.
5 years of experience building machine learning and quantitative optimization capabilities across many different product areas at scale \nExperience in machine learning, quantitative methods, control systems, or reinforcement learning \nAbility to apply and implement research concepts, ultimately in production quality code\nExperience defining clear, testable research hypotheses, including intended impact on the business\nDeep knowledge of design of experiments, online experimentation approaches, preferably at scale\nAbility to formulate and advocate for R&D objectives and results to cross-functional team members including executive business leadership and product management\nExperience contributing and/or reviewing research for top conferences and publications\nDeep fluency in Java or Python.\nExperience with Spark, Hadoop or other distributed frameworks.\nPhD in Machine Learning, Statistics, Control Theory, Forecasting, Optimization, Reinforcement Learning or related field with experience building production systems or have equivalent experience working with large data science / machine learning projects in industry.
Experience in ads optimization, recommendations, or search relevance optimization is highly preferred