Apple
Weekly Hours:
40 Role Number:
200620207-2459 Summary
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. 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 big, global brands. Because when advertising is done right, it benefits everyone! Description
We’re looking for a highly skilled and motivated Machine Learning Engineer to join our Predictions group. We build the core machine learning models that power ad predictions and monetization across Apple’s App Store and News platforms. The ideal candidate will bring deep expertise in machine learning, information retrieval, and large‑scale modeling, and will thrive in a fast‑paced, privacy‑first environment. You’ll work at the intersection of applied ML, deep learning, and retrieval systems—developing models that predict user interaction, optimize marketplace outcomes, and scale across billions of queries. You'll also explore and operationalize emerging techniques in Large Language Models (LLMs), Reinforcement Learning, and representation learning to advance Apple’s ad prediction systems. Minimum Qualifications
4+ years of experience applying machine learning and statistical modeling at scale, preferably in ad tech, recommender systems, or web‑scale search/retrieval Deep experience with neural network architectures (e.g., Transformers, DNNs, RNNs) and training pipelines using TensorFlow, PyTorch Practical understanding of reinforcement learning, explore/exploit strategies, and bandit‑based optimization Experience working with high‑volume data pipelines, A/B testing infrastructure, and performance measurement at scale Proficient in Python and familiar with SQL, Scala, or Java for production environments Ability to translate abstract ideas into concrete, high‑impact solutions Bachelor’s, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field. Preferred Qualifications
MS or PhD, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field. Great foundation in information retrieval, including query‑document matching, embedding‑based ranking, and learning‑to‑rank algorithms is a plus 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 (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf).
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40 Role Number:
200620207-2459 Summary
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. 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 big, global brands. Because when advertising is done right, it benefits everyone! Description
We’re looking for a highly skilled and motivated Machine Learning Engineer to join our Predictions group. We build the core machine learning models that power ad predictions and monetization across Apple’s App Store and News platforms. The ideal candidate will bring deep expertise in machine learning, information retrieval, and large‑scale modeling, and will thrive in a fast‑paced, privacy‑first environment. You’ll work at the intersection of applied ML, deep learning, and retrieval systems—developing models that predict user interaction, optimize marketplace outcomes, and scale across billions of queries. You'll also explore and operationalize emerging techniques in Large Language Models (LLMs), Reinforcement Learning, and representation learning to advance Apple’s ad prediction systems. Minimum Qualifications
4+ years of experience applying machine learning and statistical modeling at scale, preferably in ad tech, recommender systems, or web‑scale search/retrieval Deep experience with neural network architectures (e.g., Transformers, DNNs, RNNs) and training pipelines using TensorFlow, PyTorch Practical understanding of reinforcement learning, explore/exploit strategies, and bandit‑based optimization Experience working with high‑volume data pipelines, A/B testing infrastructure, and performance measurement at scale Proficient in Python and familiar with SQL, Scala, or Java for production environments Ability to translate abstract ideas into concrete, high‑impact solutions Bachelor’s, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field. Preferred Qualifications
MS or PhD, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field. Great foundation in information retrieval, including query‑document matching, embedding‑based ranking, and learning‑to‑rank algorithms is a plus 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 (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf).
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