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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","responsibilities":"Participates in a rotating on-call schedule, including occasional weekend coverage when necessary
Currently headquartered in Cupertino, with active expansion in Bangalore to support global operations across time zones
Leverages a diverse stack including open-source tools, commercial solutions, and internally developed systems
Encourages open dialogue, values strong ideas, and recognizes impactful results
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
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
