Machine Learning Engineer - Ads

Apple • Cupertino, CA

87d

About The Position

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! The Apple Ads team is seeking a strategic, hands-on Machine Learning Engineer to drive innovation across a modern, large-scale platform. You will design, build, and operate real-time ML systems and large-scale data pipelines that power end-to-end prediction and decisioning—spanning personalization, retrieval/ranking, allocation, and optimization—while upholding strong reliability, privacy, and safety standards. You’ll define and execute an innovation roadmap; productionize 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. This role rewards ownership from architecture through monitoring and SLAs, with influence across adjacent areas such as recommendations, response prediction, and experimentation tooling.

Requirements

• 7+ years of experience building machine learning capabilities across many different product areas at scale.
• Background in Advertising systems.
• Hands-on experience with service reliability engineering (SRE) and SLA monitoring.
• Contributions to open-source algorithm frameworks or data processing tools.

Responsibilities

• Design, develop, and optimize distributed algorithms and data processing frameworks (e.g., Spark).
• Implement scalable data pipelines to ingest, clean, transform, and analyze massive datasets.
• Collaborate with machine learning engineers to deploy and operationalize algorithms in production.
• Own the full lifecycle of services—from architecture to monitoring—for high-throughput, low-latency 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.

What This Job Offers

• Career Level: Senior
• Number of Employees: 5,001-10,000 employees