Data Platform SRE, AI & Data Platforms (AiDP)

Join the AI and Data Platforms team at Apple, where we build and manage cloud-based data platforms handling petabytes of data at scale. We are looking for a passionate and independent Software Engineer specializing in reliability engineering for data platforms, with a strong understanding of data and ML systems. If you thrive in a fast-paced environment, love crafting solutions that don't yet exist, and possess excellent communication skills to collaborate across diverse teams, we invite you to contribute to Apple's high standards in an exciting and dynamic setting.

As a Data Platform SRE, you will be responsible for developing and operating our big data platform using open source or other solutions to aid critical applications, such as analytics, reporting, and AI/ML apps. This includes working to optimize performance and cost, automate operations, and identifying and resolving production errors and issues to ensure the best data platform experience.

3+ years of professional software engineering experience with large-scale big data platforms, including strong programming skills in Java, Scala, Python, or Go.\nProven expertise in designing, building, and operating large-scale distributed data processing systems with a strong focus on Apache Spark.\nHands-on experience with table formats and data lake technologies such as Apache Iceberg, ensuring scalability, reliability, and optimized query performance.\nSkilled at coding for distributed systems and developing resilient data pipelines.\nStrong background in incident management, including troubleshooting, root cause analysis, and performance optimization in complex production environments.\nProficient with Unix/Linux systems and command-line tools for debugging and operational support.

Expertise in designing, building, and operating critical, large-scale distributed systems with a focus on low latency, fault-tolerance, and high availability.\nExperience with contribution to Open Source projects is a plus.\nExperience with multiple public cloud infrastructure, managing multi-tenant Kubernetes clusters at scale and debugging Kubernetes/Spark issues.\nExperience with workflow and data pipeline orchestration tools (e.g., Airflow, DBT).\nUnderstanding of data modeling and data warehousing concepts.\nFamiliarity with the AI/ML stack, including GPUs, MLFlow, or Large Language Models (LLMs).\nA learning attitude to continuously improve the self, team, and the organization.\nSolid understanding of software engineering best practices, including the full development lifecycle, secure coding, and experience building reusable frameworks or libraries.