What are the responsibilities and job description for the Senior Machine Learning Engineer - Ads Predictions 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! \\n\\nOur 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!
We're is 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.\n\nYou'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.\n
6 years of experience applying machine learning and statistical modeling at scale, preferably in ad tech, recommender systems, or web-scale search/retrieval\nDeep experience with neural network architectures (e.g., Transformers, DNNs, RNNs) and training pipelines using TensorFlow, PyTorch\nPractical understanding of reinforcement learning, explore/exploit strategies, and bandit-based optimization\nExperience working with high-volume data pipelines, A/B testing infrastructure, and performance measurement at scale\nProficient in Python and familiar with SQL, Scala, or Java for production environments\nAbility to translate abstract ideas into concrete, high-impact solutions \nBachelor's, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field.
MS or PhD, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field.\nGreat foundation in information retrieval, including query-document matching, embedding-based ranking, and learning-to-rank algorithms is a plus
We're is 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.\n\nYou'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.\n
6 years of experience applying machine learning and statistical modeling at scale, preferably in ad tech, recommender systems, or web-scale search/retrieval\nDeep experience with neural network architectures (e.g., Transformers, DNNs, RNNs) and training pipelines using TensorFlow, PyTorch\nPractical understanding of reinforcement learning, explore/exploit strategies, and bandit-based optimization\nExperience working with high-volume data pipelines, A/B testing infrastructure, and performance measurement at scale\nProficient in Python and familiar with SQL, Scala, or Java for production environments\nAbility to translate abstract ideas into concrete, high-impact solutions \nBachelor's, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field.
MS or PhD, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field.\nGreat foundation in information retrieval, including query-document matching, embedding-based ranking, and learning-to-rank algorithms is a plus