ML Platform SRE: Scale, Reliability & Automation

Apple Inc.; New York; On-site; USD 150,000 - 225,000; Full time

Benefits offered by this job

Medical and dental coverage

Employee stock programs

Relocation assistance

Tuition reimbursement

Retirement benefits

Job summary

Apple Ads seeks an experienced Site Reliability Engineer to own ML infrastructure health, performance, and scalability in AWS-based environments. You will build automation, improve platform resilience, and enable faster product delivery across ML training, inference, and serving workloads.

Collaborate with Ads engineering, data science, and ML platform teams to design scalable tooling beyond CI/CD, driving reliability and cost-efficiency in a large-scale production environment.

Qualifications

• 3+ years in internet-facing backend production systems, SRE or ML Ops on large-scale cloud infra.
• Proven AWS infrastructure experience.
• Familiar with ML lifecycle and NVIDIA Triton, Ray, Apache Airflow.
• Strong programming skills in Python, Java, Rust, or Go.
• Hands-on Linux and deep knowledge of its internals.
• Demonstrated experience with Infrastructure as Code, especially Terraform.
• Solid foundation in SRE concepts: monitoring, incident response, SLAs/SLOs.

Responsibilities

• Build and operate distributed systems on AWS with EKS, Elasticache and ML tech.
• Develop internal tooling for reliability, cost-efficiency, and observability.
• Collaborate with engineering to define infra architecture and troubleshoot complex issues.
• Design and manage Infrastructure as Code with Terraform for repeatable deployments.
• Lead or participate in incident response, postmortems, and continuous improvement.

Skills

SRE experience

AWS expertise

ML lifecycle familiarity

Programming in Python/Java/Rust/Go

Linux systems

Terraform / IaC

Tools

NVIDIA Triton

Ray

Apache Airflow

Kubernetes

Terraform

Job description

Apple Ads seeks an experienced Site Reliability Engineer to own ML infrastructure health, performance, and scalability in AWS-based environments. You will build automation, improve platform resilience, and enable faster product delivery across ML training, inference, and serving workloads.

Collaborate with Ads engineering, data science, and ML platform teams to design scalable tooling beyond CI/CD, driving reliability and cost-efficiency in a large-scale production environment.