Staff Machine Learning Engineer - Ads PlatformApple • Austin, Texas, United States

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Staff Machine Learning Engineer - Ads Platform

Apple

Austin, Texas, United States

Austin, Texas, United States

About

Austin, Texas, United States Machine Learning and AI At Apple, we focus deeply on the customer 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, MLS Season Pass and now F1 ! . Everything 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 news publishers to big, global brands. Because when advertising is done right, it benefits everyone. Description Apple Ads is Hiring a hands-on Machine Learning Engineer. In this role you will build design and build Machine learning systems and data pipelines to safeguard the advertiser trust of our platform and enhance invalid traffic protections. You will define and execute an innovation roadmap; build and deploy models with robust CI/CD, feature stores, and streaming infrastructure (e.g., Kafka/Spark/Flink); and run A/B experimentation. You will lead performance tuning, calibration, and drift detection to deliver measurable improvements in product quality, user experience, latency, and cost. Responsibilities Develop and manage end-to-end lifecycle of machine learning models, including observability for large-scale, high-throughput, and low-latency production systems. Design, develop, and optimize distributed algorithms and data processing frameworks(e.g., Spark). Implement scalable feature pipelines to ingest, clean, transform, and analyze massive datasets. Reinforce Ads integrity and advertiser trust by safeguarding infrastructure. Solve complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks Stay up to date with developments in the machine learning industry Collaborate with product and engineering teams on production systems and applications. Drive performance optimization, bottleneck analysis, and system tuning across compute and storage layers. Build tools to support A/B testing, statistical evaluation, and experimentation pipelines. Ensure data integrity, security, and compliance across all solutions. Participate in cross-functional Agile teams to prototype and deliver impactful, data-driven products. Minimum Qualifications 8+ years of experience building machine learning capabilities across many different product areas at scale Strong proficiency in Java, Python, or Scala for algorithm and system development. Experience with distributed systems and big data frameworks such as Spark, Kafka, Hadoop, or Flink. Solid understanding of data structures, algorithms, and system design principles. Familiarity with CI/CD workflows, cloud environments, and containerized deployments. Knowledge of data validation, cleansing, and quality assurance practices. Understanding of statistical methods, A/B testing, and online experimentation frameworks. Prior experience working with Anomaly detection is a plus. BS or MS in Computer Science, Software Engineering or related technical fields. Preferred Qualifications 10+ years of experience building machine learning capabilities across many different product areas at scale. Background in Advertising systems. Contributions to open-source algorithm frameworks or data processing tools. Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant . Apple accepts applications to this posting on an ongoing basis.
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Austin, Texas, United States

Languages

English

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