Apple is seeking an AI Data Platform Engineer to design, build, and operate scalable AI data platforms and services that support AI model development and production. You will develop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data, and create AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning, and metadata management. Implement data quality frameworks, validation pipelines, observability, and evaluation metrics to ensure trusted AI datasets. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications. Build scalable capabilities for end-to-end AI data lifecycle management, including governance, lineage, automated validation, and secure publishing of AI-ready datasets. Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to translate AI data requirements into production-ready data solutions. Optimize platform scalability, reliability, performance, security, and cost across cloud-native environments. Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence. Evaluate emerging AI technologies to continuously improve platform capabilities that enable GenAI, agentic AI, and embodied AI solutions.

Apple is seeking an AI Data Platform Engineer to design, build, and operate scalable AI data platforms and services that support AI model development and production. You will develop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data, and create AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning, and metadata management. Implement data quality frameworks, validation pipelines, observability, and evaluation metrics to ensure trusted AI datasets. Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications. Build scalable capabilities for end-to-end AI data lifecycle management, including governance, lineage, automated validation, and secure publishing of AI-ready datasets. Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to translate AI data requirements into production-ready data solutions. Optimize platform scalability, reliability, performance, security, and cost across cloud-native environments. Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence. Evaluate emerging AI technologies to continuously improve platform capabilities that enable GenAI, agentic AI, and embodied AI solutions.