Job Description

Locations

• Cupertino, California, United States 95014
• New York City, New York, United States 10022

Salary

Skills Required

• strategic thinkingintermediate
• collaborationintermediate
• technical leadershipintermediate
• large-scale data architectureintermediate
• AI/ML infrastructureintermediate
• mentoringintermediate
• team growthintermediate
• ML engineeringintermediate
• data engineeringintermediate
• data ingestionintermediate
• data transformationintermediate
• model trainingintermediate
• model evaluationintermediate
• production monitoringintermediate
• privacy-preserving systemsintermediate
• high-quality data managementintermediate
• scalable platform developmentintermediate
• AI/ML strategyintermediate

Required Qualifications

• 6+ years leading engineering teams that build large-scale data infrastructure or ML platforms for enterprise environments. (experience, 6 years)
• Proven experience designing multi-use platform services and influencing cross-team technical roadmaps. (experience)
• Strong hands-on expertise with Java, Python, or Scala, and with data architecture, modeling, and SQL. (experience)
• 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)
• Experience in both batch and real-time data processing, including CI/CD environments and cloud-native data systems. (experience)
• Demonstrated experience contributing to ML platforms supporting data pipelines, model training, serving, and monitoring. (experience)
• Strong understanding of AI/ML data management, including handling unstructured data, dataset versioning, and training data quality at scale. (experience)
• Hands-on experience building model monitoring and observability systems for drift detection, model degradation, and real-time prediction quality. (experience)
• Familiarity with annotation and labeling workflows, as well as generative AI techniques such as transformer architectures, diffusion models, and multimodal learning. (experience)
• Proven ability to lead teams delivering mission-critical production services with high reliability and operational excellence. (experience)
• Experience working closely with operations teams on deployment, monitoring, and system reliability. (experience)
• Strong analytical and problem-solving skills with a track record of data-driven architectural decisions. (experience)
• BS or equivalent experience in Computer Science, Data Engineering, Machine Learning, or a related field. (experience)

Preferred Qualifications

• Experience collaborating with ML researchers, data scientists, and product engineers on ML solutions. (experience)
• Expertise in data synthesis, fine-tuning, and data management for foundation models and LLMs, including multimodal workflows. (experience)
• Knowledge of data privacy and differential privacy in AI/ML systems. (experience)
• Practical experience developing or partnering on production ML models. (experience)
• Demonstrated ability to influence and foster collaboration across large, cross-functional teams. (experience)
• Experience in advertising technology, recommendation systems, or other applied ML domains. (experience)

Responsibilities

• As an engineering manager, you will lead the design and delivery of scalable data foundations that support AI/ML across Apple Ads. Your team will build and operate platform services that span the full ML data lifecycle—from ingestion and transformation to training, evaluation, and production monitoring—ensuring systems are reliable, privacy-preserving, and built on high-quality data.
• This role combines deep technical expertise in large-scale data architecture with the ability to develop a cohesive strategy for AI/ML infrastructure at enterprise scale. You will mentor and grow a team of ML and Data Engineers to deliver core data services, tooling, and frameworks that help Apple Ads teams develop, train, and deploy models effectively.
• 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.
• Implement privacy-preserving data processing techniques aligned with Apple’s privacy standards.
• Establish best practices for data governance, lineage tracking, and reproducibility across ML workflows.
• Collaborate across teams to define the roadmap for AI/ML data foundations across the Ads ecosystem.
• Lead and mentor a team of ML and Data Engineers, fostering technical growth and collaboration.
• Partner with cross-functional teams to identify shared data services that accelerate model development.
• Encourage innovation in data management and infrastructure design, including handling unstructured and multimodal data at scale.

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