Machine Learning Engineer - Ads
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
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.
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This role rewards ownership from architecture through monitoring and SLAs, with influence across adjacent areas such as recommendations, response prediction, and experimentation tooling.","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.
Preferred Qualifications
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.
Minimum Qualifications
4+ 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.
Expertise in working with relational databases (PostgreSQL, MySQL, Oracle) and NoSQL/Cloud storage (S3, GCS, etc.).
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 machine learning platforms or real-time recommendation engines is a plus.
BS or MS in Computer Science, Software Engineering or related technical fields.
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 .
Perks and Benefits
Health and Wellness
Parental Benefits
Work Flexibility
Office Life and Perks
Vacation and Time Off
Financial and Retirement
Professional Development
Diversity and Inclusion
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