Apple Ads group needs to help users discover new content seamlessly while supporting publishers and developers in promoting and monetizing their work. The Ads Machine Learning Platform team's mission is to help Ads teams develop, deploy, and operate innovative AI/ML applications efficiently and at scale. This role is to guide the development of the data foundations that power these AI/ML initiatives.
Requirements
- Strong hands-on expertise with Java, Python, or Scala, and with data architecture, modeling, and SQL.
- Deep technical proficiency in data processing frameworks (Spark, Flink), streaming systems (Kafka), data lakes/warehouses (Iceberg, Delta Lake), databases (Cassandra, Redis), and workflow orchestration tools.
- Experience in both batch and real-time data processing, including CI/CD environments and cloud-native data systems.
- Demonstrated experience contributing to ML platforms supporting data pipelines, model training, serving, and monitoring.
- Strong understanding of AI/ML data management, including handling unstructured data, dataset versioning, and training data quality at scale.
- Hands-on experience building model monitoring and observability systems for drift detection, model degradation, and real-time prediction quality.
- Familiarity with annotation and labeling workflows, as well as generative AI techniques such as transformer architectures, diffusion models, and multimodal learning.
Responsibilities
- Build and scale data management systems using technologies such as Spark, Iceberg, and Kafka to support AI/ML workloads.
- Develop data quality frameworks for automated validation, drift detection, and anomaly monitoring across training and production.
- Design production model monitoring systems to track data drift, model performance, and prediction quality in real time.
- Build training data services for LLMs, multimodal models, and classical ML use cases.
- Implement feature engineering and data processing tools to ensure consistent training and serving pipelines.
- Build and support A/B testing and experimentation platforms to measure model and feature performance.
- Develop annotation, labeling, and data augmentation pipelines to support model development and fine-tuning.
Other
- 6+ years leading engineering teams that build large-scale data infrastructure or ML platforms for enterprise environments.
- Proven experience designing multi-use platform services and influencing cross-team technical roadmaps.
- Proven ability to lead teams delivering mission-critical production services with high reliability and operational excellence.
- Experience working closely with operations teams on deployment, monitoring, and system reliability.
- Strong analytical and problem-solving skills with a track record of data-driven architectural decisions.
