At Apple, we don’t just build products — we create transformative experiences that have reshaped entire industries. Our innovation is driven by the diversity of our people and their ideas, inspiring everything we do. Imagine the impact you could make. Join Apple and help us leave the world better than we found it. The ML Infrastructure team is responsible for managing Apple’s largest ML compute platform, multi-cloud storage abstraction and caching platform, which supports critical machine learning training workloads that power user-facing features across the Apple ecosystem. Operating across both first-party and third-party cloud environments brings complex and unique challenges. As a Site Reliability Engineer (SRE) on the ML Infrastructure team, you’ll be expected to address these challenges through a strong foundation in cloud object storage, data analysis, automation, collaboration, and advanced expertise in Kubernetes. Our team oversees the full infrastructure stack — from low-level nodes to the complete network architecture — ensuring our platform remains highly available, resilient, and efficient at scale.
Description
We are seeking an experienced Software and Systems Engineer to join our dynamic team. This role demands a proactive mindset, technical excellence, and a collaborative spirit. The ideal candidate will demonstrate: • Strong critical thinking and a high degree of individual accountability • Effective communication and collaboration skills • A genuine passion for Infrastructure as a Service (IaaS) • A commitment to automation and operational efficiency • Ownership of projects from design through delivery • A solutions-oriented approach, coupled with the ability to gain alignment on technical direction • Consistent and timely execution of design implementations aligned with project objectives • The ability to provide constructive technical feedback, fostering team-wide growth and continuous improvement
Minimum qualifications
5+ years experience in building, operating and scaling a large application in a
private, public or hybrid cloud environment
Deep expertise in Kubernetes, with hands-on experience using platforms such
as Google Kubernetes Engine (GKE) or Amazon Elastic Kubernetes Service
(EKS)
Proficient in designing, developing, and releasing code in languages such as
Python, Go, or Rust
Practical experience with object storage technologies, including Amazon S3
or Google Cloud Storage (GCS)
Strong background in designing and troubleshooting complex networking
issues in both public and private cloud infrastructures
Solid understanding of Linux internals, standard networking protocols, and
distributed systems architecture
Preferred qualifications
Preferred Qualifications
Proven drive to automate manual operations and enhance processes through
continuous iteration
Strong understanding of best practices for deploying large-scale, distributed
applications
Hands-on experience managing diverse system environments using
configuration management tools or software delivery platforms such as
Spinnaker, Helm, or Flux
Demonstrated expertise in deploying, supporting, and monitoring both new
and existing services, platforms, and application stacks
Solid familiarity with container orchestration and management using
Kubernetes