About this opportunity
Apple lists this Staff Machine Learning Engineer – Ads Predictions opportunity in cupertino, California. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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 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.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.
Key Responsibilities
Design and implement ML models to improve predictions of user interaction, click-through rate (CTR), and conversion rate (CVR)
Develop and optimize retrieval algorithms, leveraging techniques from classical IR and modern deep learning
Contribute to core modeling areas such as deep neural networks, contextual bandits, multi-task learning, and LLM-based ranking signals
Work with large-scale, distributed datasets to identify new signals and improve model accuracy and robustness
Collaborate with cross-functional teams across engineering, infrastructure, and product to scale models to production
Participate in designing and running large-scale experiments to validate new model architectures and learning strategies
Minimum Qualifications
8+ 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
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Worksite address
cupertino, CA, 95014, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.