Software Engineer, Platform Reliability Engineering, AiDP

AI & Data Platforms (AiDP) is IS&Ts 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.

The Applied Machine Learning team in AI and Data Platform organization is building the foundation for Apples enterprise-wide machine learning and data capabilities. Our Applied Machine Learning team designs, builds, and operates mission-critical platforms and services spanning ML, GenAI, inference, and big data-enabling teams across the company to harness AI and analytics at scale. We tackle complex technical challenges in reliability, performance, and scalability across a diverse ecosystem of open source and cutting-edge technologies, serving some of Apples most demanding workloads.

Were seeking an experienced software engineer to join our Platform Reliability Engineering team and drive the design, operation, and optimization of large-scale distributed systems that power our GenAI, ML, and big data platforms. Youll leverage cutting-edge open source technologies in hybrid cloud environments to build resilient infrastructure that enables seamless inference, data processing, and machine learning workloads at scale. In this role, youll own mission-critical platform components, respond to production incidents, and collaborate across teams to shape the future of our data and AI infrastructure. Design, build, and maintain scalable multi-tenant systems that support diverse workloads and technologies at enterprise scale Own the full lifecycle of infrastructure and platform projects-from architectural design and implementation through deployment, monitoring, and optimization Operate and optimize high-throughput, mission-critical services to ensure reliability, performance, and cost-efficiency Participate in on-call rotations to respond to production incidents; diagnose root causes, implement rapid fixes, and drive post-incident improvements Lead cross-functional collaboration with engineering teams to define requirements, validate designs, and deliver customer-impacting features and improvements Proactively identify operational bottlenecks and systemic issues; implement preventive measures to reduce incident frequency and improve system resilience Establish observability practices and continuously refine operational excellence standards across the platformBachelors degree in Computer Science, Computer Engineering, or equivalent professional experience Proficiency in at least one systems programming language (Python, Go, Java, or similar) Strong expertise in distributed systems architecture, with deep knowledge of reliability, scalability, and containerization principles Hands-on experience with cloud platforms and data processing infrastructure (Kubernetes, Spark, Flink, Ray, Trino, or equivalent technologies) 7+ years of experience in SRE, DevOps, or infrastructure engineering, with demonstrated expertise managing distributed systems at scale. Proficiency in diagnosing and resolving complex production incidents and performance bottlenecks in large-scale distributed environments. Familiarity with open source codebases; ability to read, understand, and explain complex system implementations Strong understanding of system architecture and proven ability to collaborate effectively across engineering teams Hands-on experience with big data technologies (Spark, Flink, Iceberg) and/or ML/AI platforms (Ray, MLflow, model serving infrastructure). Strong foundational knowledge of Linux, databases, and security principles Proactive mindset with demonstrated commitment to optimizing reliability and uptime for mission-critical services Excellent written and verbal communication skills with ability to articulate technical concepts and strategies to both engineering teams and non-technical leadership Demonstrated track record of designing and operating systems at scale