Software Site Reliability Engineer - AI & Data Platforms

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless. Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here. AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning — including generative AI — along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple.

Minimum Qualifications

Experience: 3+ years in software site reliability engineering or software development roles. Programming: Proficient in at least one of Python, Golang, or Java. Skilled at coding for distributed systems and developing resilient data pipelines. Cloud Platforms: Hands-on experience with at least one major cloud platform (AWS, Azure, or Google Cloud Platform).

Preferred Qualifications

Expertise in designing, building, and operating critical, large-scale distributed systems with a focus on low latency, fault-tolerance, and high availability. Experience with contribution to Open Source projects is a plus. Experience with multiple public cloud infrastructure, managing multi-tenant Kubernetes clusters at scale and debugging Kubernetes/Spark issues. Experience with workflow and data pipeline orchestration tools (e.g., Airflow, DBT). Understanding of data modeling and data warehousing concepts. Familiarity with the AI/ML stack, including GPUs, MLFlow, or Large Language Models (LLMs). Data Structures & Algorithms: Strong foundation and application experience. Distributed Systems: Solid understanding and hands-on experience managing at least one distributed system (e.g. Kafka, Spark, Flink etc. ). Solid understanding of software engineering best practices, including the full development lifecycle, secure coding, and experience building reusable frameworks or libraries. Problem Solving: Demonstrated ability to independently troubleshoot and resolve complex technical issues. Creative Thinking: A track record of proposing and implementing innovative solutions to technical challenges.

Description

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 issues to ensure the best data platform experience

Responsibilities

Design, develop, and automate: Build tools, frameworks and solutions to improve reliability, scalability, and efficiency across large scale distributed data platform systems. Monitor and maintain: Implement advanced monitoring and alerting for on-prem , cloud and workloads. Troubleshoot and solve: Support critical applications including analytics, reporting, and AI/ML apps. Respond to and resolve complex production incidents, and perform root cause analysis. Collaborate: Work closely with development and operations teams to integrate reliability best practices throughout the software lifecycle. Optimize: Proactively recommend improvements in architecture, deployment, and operations for distributed systems